{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <div align='center'>第4章 回归分析</div>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 逐步回归与模型选择\n",
    "当回归模型的自变量过多时，需要压缩自变量，方法主要有两种：一是逐步回归； 二是模型选择的正则化方法，比如岭回归和Lasso。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### 4.3.1 逐步回归\n",
    " "
   ]
  },
  {
   "attachments": {
    "4.%E5%9B%9E%E5%BD%92%E5%88%86%E6%9E%903_1.jpg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### 示例：\n",
    "![4.%E5%9B%9E%E5%BD%92%E5%88%86%E6%9E%903_1.jpg](attachment:4.%E5%9B%9E%E5%BD%92%E5%88%86%E6%9E%903_1.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "使用调整后R方作为标准选择最佳模型，\n",
    "也可以使用AIC,BIC,PRESS等统计量\n",
    "'''\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "'''\n",
    "data: Pandas的DataFrame类型数据\n",
    "response: 因变量(响应变量)\n",
    "'''\n",
    "def forward_selected(data, response):\n",
    "    #remaining = set(data.columns)\n",
    "    ###自变量名称，从DataFrame取出\n",
    "    remaining=list(data.columns)\n",
    "    ###从数据里去掉响应变量，作为自变量列表\n",
    "    remaining.remove(response)\n",
    "    selected = []\n",
    "    current_score, best_new_score = 0.0, 0.0\n",
    "    print('-----------初始变量列表：',remaining)\n",
    "    i=1\n",
    "    while remaining and current_score == best_new_score:\n",
    "        scores_with_candidates = []\n",
    "        ###从剩余变量中弹出一个变量迭代加入模型\n",
    "        for candidate in remaining:\n",
    "            formula = \"{} ~ 1 + {}\".format(response, ' + '.join(selected + [candidate]))\n",
    "            result = smf.ols(formula, data).fit()\n",
    "            score = result.rsquared_adj\n",
    "            aic = result.aic\n",
    "            #press准则：Prediction Error of Square Sum\n",
    "            #预测误差平方和\n",
    "            press=np.sum(result.get_influence().resid_press**2)\n",
    "            scores_with_candidates.append((score, candidate))\n",
    "            print('\\n模型：',formula,'\\n调整后R方：',np.round(score,3),\n",
    "                  ', AIC:',np.round(aic,3),'PRESS:',np.round(press,3))\n",
    "        ###对模型按照score排序，list对象的sort函数是从小到大排序\n",
    "        scores_with_candidates.sort()\n",
    "        ###弹出一个变量及其评分，注意list的pop函数是弹出最后一个元素，即最大评分的自变量组合\n",
    "        best_new_score, best_candidate = scores_with_candidates.pop()\n",
    "        \n",
    "        ###通过rsquared_adj比较模型自变量增减前后的模型效果。\n",
    "        ###如果rsquared_adj增加，则从selected中移除候选模型，增加新的最佳模型。\n",
    "        if current_score < best_new_score:\n",
    "            remaining.remove(best_candidate)\n",
    "            selected.append(best_candidate)\n",
    "            current_score = best_new_score\n",
    "        print('-----------remaining',i,'-',remaining)\n",
    "        print('+++++++++++selected',i,'-',selected)\n",
    "        i=i+1\n",
    "    formula = \"{} ~ 1+ {}\".format(response,' + '.join(selected))\n",
    "    model = smf.ols(formula, data).fit()\n",
    "    return model,scores_with_candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------初始变量列表： ['X1', 'X2', 'X3', 'X4']\n",
      "\n",
      "模型： Y ~ 1 + X1 \n",
      "调整后R方： 0.492 , AIC: 100.412 PRESS: 1699.612\n",
      "\n",
      "模型： Y ~ 1 + X2 \n",
      "调整后R方： 0.636 , AIC: 96.07 PRESS: 1202.087\n",
      "\n",
      "模型： Y ~ 1 + X3 \n",
      "调整后R方： 0.221 , AIC: 105.96 PRESS: 2616.364\n",
      "\n",
      "模型： Y ~ 1 + X4 \n",
      "调整后R方： 0.645 , AIC: 95.744 PRESS: 1194.218\n",
      "-----------remaining 1 - ['X1', 'X2', 'X3']\n",
      "+++++++++++selected 1 - ['X4']\n",
      "\n",
      "模型： Y ~ 1 + X4 + X1 \n",
      "调整后R方： 0.967 , AIC: 65.634 PRESS: 121.224\n",
      "\n",
      "模型： Y ~ 1 + X4 + X2 \n",
      "调整后R方： 0.616 , AIC: 97.522 PRESS: 1461.814\n",
      "\n",
      "模型： Y ~ 1 + X4 + X3 \n",
      "调整后R方： 0.922 , AIC: 76.745 PRESS: 294.014\n",
      "-----------remaining 2 - ['X2', 'X3']\n",
      "+++++++++++selected 2 - ['X4', 'X1']\n",
      "\n",
      "模型： Y ~ 1 + X4 + X1 + X2 \n",
      "调整后R方： 0.976 , AIC: 61.866 PRESS: 85.351\n",
      "\n",
      "模型： Y ~ 1 + X4 + X1 + X3 \n",
      "调整后R方： 0.975 , AIC: 62.62 PRESS: 94.537\n",
      "-----------remaining 3 - ['X3']\n",
      "+++++++++++selected 3 - ['X4', 'X1', 'X2']\n",
      "\n",
      "模型： Y ~ 1 + X4 + X1 + X2 + X3 \n",
      "调整后R方： 0.974 , AIC: 63.837 PRESS: 110.347\n",
      "-----------remaining 4 - ['X3']\n",
      "+++++++++++selected 4 - ['X4', 'X1', 'X2']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\users\\hp\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\scipy\\stats\\stats.py:1542: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=13\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>Y</td>        <th>  R-squared:         </th> <td>   0.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.976</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   166.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 16 Aug 2021</td> <th>  Prob (F-statistic):</th> <td>3.32e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:11:36</td>     <th>  Log-Likelihood:    </th> <td> -26.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    13</td>      <th>  AIC:               </th> <td>   61.87</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     9</td>      <th>  BIC:               </th> <td>   64.13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   71.6483</td> <td>   14.142</td> <td>    5.066</td> <td> 0.001</td> <td>   39.656</td> <td>  103.641</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X4</th>        <td>   -0.2365</td> <td>    0.173</td> <td>   -1.365</td> <td> 0.205</td> <td>   -0.629</td> <td>    0.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X1</th>        <td>    1.4519</td> <td>    0.117</td> <td>   12.410</td> <td> 0.000</td> <td>    1.187</td> <td>    1.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X2</th>        <td>    0.4161</td> <td>    0.186</td> <td>    2.242</td> <td> 0.052</td> <td>   -0.004</td> <td>    0.836</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.211</td> <th>  Durbin-Watson:     </th> <td>   2.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.900</td> <th>  Jarque-Bera (JB):  </th> <td>   0.378</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.202</td> <th>  Prob(JB):          </th> <td>   0.828</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.270</td> <th>  Cond. No.          </th> <td>1.27e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.27e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      Y   R-squared:                       0.982\n",
       "Model:                            OLS   Adj. R-squared:                  0.976\n",
       "Method:                 Least Squares   F-statistic:                     166.8\n",
       "Date:                Mon, 16 Aug 2021   Prob (F-statistic):           3.32e-08\n",
       "Time:                        12:11:36   Log-Likelihood:                -26.933\n",
       "No. Observations:                  13   AIC:                             61.87\n",
       "Df Residuals:                       9   BIC:                             64.13\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     71.6483     14.142      5.066      0.001      39.656     103.641\n",
       "X4            -0.2365      0.173     -1.365      0.205      -0.629       0.155\n",
       "X1             1.4519      0.117     12.410      0.000       1.187       1.717\n",
       "X2             0.4161      0.186      2.242      0.052      -0.004       0.836\n",
       "==============================================================================\n",
       "Omnibus:                        0.211   Durbin-Watson:                   2.011\n",
       "Prob(Omnibus):                  0.900   Jarque-Bera (JB):                0.378\n",
       "Skew:                           0.202   Prob(JB):                        0.828\n",
       "Kurtosis:                       2.270   Cond. No.                     1.27e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.27e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "###导入数据\n",
    "X1=np.array([7,  1, 11, 11,  7, 11,  3,  1,  2, 21,  1, 11, 10])\n",
    "X2=np.array([26, 29, 56, 31, 52, 55, 71, 31, 54, 47, 40, 66, 68])\n",
    "X3=np.array([6, 15,  8,  8,  6,  9, 17, 22, 18,  4, 23,  9,  8])\n",
    "X4=np.array([60, 52, 20, 47, 33, 22,  6, 44, 22, 26, 34, 12, 12])\n",
    "Y =np.array([78.5, 74.3, 104.3,  87.6,  95.9, 109.2, 102.7, 72.5,\n",
    "            93.1,115.9,  83.8, 113.3, 109.4])\n",
    "data=pd.DataFrame(np.array([X1,X2,X3,X4,Y]).T,columns=['X1','X2','X3','X4','Y'])\n",
    "model,swc = forward_selected(data, 'Y')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      Y   R-squared:                       0.979\n",
      "Model:                            OLS   Adj. R-squared:                  0.974\n",
      "Method:                 Least Squares   F-statistic:                     229.5\n",
      "Date:                Mon, 16 Aug 2021   Prob (F-statistic):           4.41e-09\n",
      "Time:                        12:11:38   Log-Likelihood:                -28.156\n",
      "No. Observations:                  13   AIC:                             62.31\n",
      "Df Residuals:                      10   BIC:                             64.01\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     52.5773      2.286     22.998      0.000      47.483      57.671\n",
      "X1             1.4683      0.121     12.105      0.000       1.198       1.739\n",
      "X2             0.6623      0.046     14.442      0.000       0.560       0.764\n",
      "==============================================================================\n",
      "Omnibus:                        1.509   Durbin-Watson:                   1.922\n",
      "Prob(Omnibus):                  0.470   Jarque-Bera (JB):                1.104\n",
      "Skew:                           0.503   Prob(JB):                        0.576\n",
      "Kurtosis:                       1.987   Cond. No.                         175.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "模型Y~X1+X2的PRESS： 93.883\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\users\\hp\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\scipy\\stats\\stats.py:1542: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=13\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "根据变量X4的t检验结果\n",
    "'''\n",
    "formula='Y~X1+X2'\n",
    "result=smf.ols(formula,data=data).fit()\n",
    "print(result.summary())\n",
    "\n",
    "'''\n",
    "PRESS(预测误差平方和)统计量\n",
    "'''\n",
    "press = np.round(np.sum(result.get_influence().resid_press**2),3)\n",
    "print('\\n模型Y~X1+X2的PRESS：',press)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3.2 模型压缩与正则化\n",
    "主要包含岭回归（Ridge regression）和Lasso两种方法，二者的主要原理是将系数往等于0的方向压缩。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### 示例1：岭回归\n",
    "使用信用卡数据进行岭回归。<br>\n",
    "信用卡数据字段：<br>\n",
    "Income：收入，Limit：信用额度，Rating：信用等级，Cards：信用卡数，\tAge：年龄，\tEducation：教育程度 ，Gender：性别，Student：是否学生，Married：是否已婚，Ethnicity：种族，Balance：余额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据形状： (400, 11)\n",
      "\n",
      "数据示例：\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Cards</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Student</th>\n",
       "      <th>Married</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.891</td>\n",
       "      <td>3606</td>\n",
       "      <td>283</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106.025</td>\n",
       "      <td>6645</td>\n",
       "      <td>483</td>\n",
       "      <td>3</td>\n",
       "      <td>82</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Asian</td>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104.593</td>\n",
       "      <td>7075</td>\n",
       "      <td>514</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>11</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Asian</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148.924</td>\n",
       "      <td>9504</td>\n",
       "      <td>681</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Asian</td>\n",
       "      <td>964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55.882</td>\n",
       "      <td>4897</td>\n",
       "      <td>357</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>80.180</td>\n",
       "      <td>8047</td>\n",
       "      <td>569</td>\n",
       "      <td>4</td>\n",
       "      <td>77</td>\n",
       "      <td>10</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>1151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.996</td>\n",
       "      <td>3388</td>\n",
       "      <td>259</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>12</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>African American</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>71.408</td>\n",
       "      <td>7114</td>\n",
       "      <td>512</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>9</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Asian</td>\n",
       "      <td>872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.125</td>\n",
       "      <td>3300</td>\n",
       "      <td>266</td>\n",
       "      <td>5</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>71.061</td>\n",
       "      <td>6819</td>\n",
       "      <td>491</td>\n",
       "      <td>3</td>\n",
       "      <td>41</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>African American</td>\n",
       "      <td>1350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Income  Limit  Rating  Cards  Age  Education  Gender Student Married  \\\n",
       "1    14.891   3606     283      2   34         11    Male      No     Yes   \n",
       "2   106.025   6645     483      3   82         15  Female     Yes     Yes   \n",
       "3   104.593   7075     514      4   71         11    Male      No      No   \n",
       "4   148.924   9504     681      3   36         11  Female      No      No   \n",
       "5    55.882   4897     357      2   68         16    Male      No     Yes   \n",
       "6    80.180   8047     569      4   77         10    Male      No      No   \n",
       "7    20.996   3388     259      2   37         12  Female      No      No   \n",
       "8    71.408   7114     512      2   87          9    Male      No      No   \n",
       "9    15.125   3300     266      5   66         13  Female      No      No   \n",
       "10   71.061   6819     491      3   41         19  Female     Yes     Yes   \n",
       "\n",
       "           Ethnicity  Balance  \n",
       "1          Caucasian      333  \n",
       "2              Asian      903  \n",
       "3              Asian      580  \n",
       "4              Asian      964  \n",
       "5          Caucasian      331  \n",
       "6          Caucasian     1151  \n",
       "7   African American      203  \n",
       "8              Asian      872  \n",
       "9          Caucasian      279  \n",
       "10  African American     1350  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "import statsmodels.formula.api as smf\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import patsy\n",
    "data = pd.read_csv(r'e:\\data\\credit.csv',index_col=0)\n",
    "print('数据形状：',data.shape)\n",
    "print('\\n数据示例：')\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\users\\hp\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.952</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.952</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1967.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 16 Aug 2021</td> <th>  Prob (F-statistic):</th> <td>3.06e-259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:11:42</td>     <th>  Log-Likelihood:    </th> <td> -2411.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   400</td>      <th>  AIC:               </th> <td>   4833.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   395</td>      <th>  BIC:               </th> <td>   4852.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  520.0150</td> <td>    5.052</td> <td>  102.934</td> <td> 0.000</td> <td>  510.083</td> <td>  529.947</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X[0]</th>      <td> -279.6526</td> <td>    8.288</td> <td>  -33.742</td> <td> 0.000</td> <td> -295.947</td> <td> -263.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X[1]</th>      <td>  280.5161</td> <td>   64.231</td> <td>    4.367</td> <td> 0.000</td> <td>  154.239</td> <td>  406.793</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X[2]</th>      <td>  338.4898</td> <td>   64.121</td> <td>    5.279</td> <td> 0.000</td> <td>  212.430</td> <td>  464.550</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X[3]</th>      <td>  126.8005</td> <td>    5.062</td> <td>   25.049</td> <td> 0.000</td> <td>  116.848</td> <td>  136.753</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>24.527</td> <th>  Durbin-Watson:     </th> <td>   1.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  27.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.647</td> <th>  Prob(JB):          </th> <td>8.55e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.963</td> <th>  Cond. No.          </th> <td>    29.6</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.952\n",
       "Model:                            OLS   Adj. R-squared:                  0.952\n",
       "Method:                 Least Squares   F-statistic:                     1967.\n",
       "Date:                Mon, 16 Aug 2021   Prob (F-statistic):          3.06e-259\n",
       "Time:                        12:11:42   Log-Likelihood:                -2411.3\n",
       "No. Observations:                 400   AIC:                             4833.\n",
       "Df Residuals:                     395   BIC:                             4852.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    520.0150      5.052    102.934      0.000     510.083     529.947\n",
       "X[0]        -279.6526      8.288    -33.742      0.000    -295.947    -263.359\n",
       "X[1]         280.5161     64.231      4.367      0.000     154.239     406.793\n",
       "X[2]         338.4898     64.121      5.279      0.000     212.430     464.550\n",
       "X[3]         126.8005      5.062     25.049      0.000     116.848     136.753\n",
       "==============================================================================\n",
       "Omnibus:                       24.527   Durbin-Watson:                   1.902\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               27.945\n",
       "Skew:                           0.647   Prob(JB):                     8.55e-07\n",
       "Kurtosis:                       2.963   Cond. No.                         29.6\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "使用Income,Limit,Rating,Student四个属性作为自变量，使用Balance做因变量\n",
    "构建模型，即通过收入，信用额度，信用等级和是否学生来预测余额。\n",
    "对于超参数lambda，设定从10^(-2)到10^10次方超参数集等距挑选100个数进行测试。\n",
    "'''\n",
    "X = data[['Income','Limit','Rating','Student']]\n",
    "y = data['Balance']\n",
    "###将因子变量映射成0,1\n",
    "X['Student'] = X['Student'].map({'Yes':1,'No':0})\n",
    "###对数字变量进行标准化\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "formula = 'y~X'\n",
    "model=smf.ols(formula,data={'y':y,'X':X_scaled})\n",
    "result=model.fit()\n",
    "result.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\users\\hp\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(4.326563071665612, 1e-05)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = []###残差均方\n",
    "###超参数的范围，100个，从10^-2到10^10\n",
    "lam=10**np.linspace(-5,10,100)\n",
    "###Statsmodels有一个岭回归和Lasso的简单实现即fit_regularized函数\n",
    "for l in lam:\n",
    "    ###L1_wt:0为岭回归；1为Lasso；0-1之间为ElasticNet算法\n",
    "    result = model.fit_regularized(L1_wt=0,alpha=l,refit=True,profile_scale=False)\n",
    "    ###使用原始数据进行预测\n",
    "    pred=result.predict()\n",
    "    ###计算残差均方\n",
    "    mse.append(np.sum(y-pred)**2)\n",
    "    \n",
    "sns.lineplot(np.arange(0,len(mse)),mse)\n",
    "plt.show()\n",
    "lammin=lam[np.argmin(mse)]\n",
    "###最小均方残差以及lambda超参数的值\n",
    "np.min(mse),lammin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wxrI3mLFlBpPPmswTc59gadZS3hz6JpM3TSavLI9T255KYXkh4e5wXfhIRETkALx+0uvVvnwgbr/9dsaOHUtubi4ARx99NBMmTKCoqIjY2Ng9to2Li9vjsccTuK5LSEgIZWVlnHjiicyYMYOvv/6aRx55pEp1VJegh3CgFfB/lcuTgAJgi7V2rDEmGTgTiN9H24dBqVbqjEhPJJGewIWIBjQdwICmAwC4odcN3NDrBgCu6HoFp7Q+BYAWMS12P/fztZ+zqWATp7Y9lXtn3cu6vHWMHTmWd5e/S5mvjMu6XMam/E1EuCNICEuo3RcmIiIifxASEsKHH37IwIED6d27N5dffjkff/wx48ePx1qLw+HA7z+wPtpt27bRs2dPhg4dWsNV71/Qu/6stQsqe7WPBF4B+gGLKlcvBobtp03kL6VFp5Gekg7Ame3O5NY+twLw9MCneX1I4C/wk1uezIWdLgRg0c5FzN0+F4B7Z93LTT/cBMBjcx7j2QXPAjB3+1xW7lpZmy9DRETksDV+/HgAxo0bR7t27XjssccYNWoUCQkJXHvtteTl5fHxxx8TExNDUVERr732GnPmzGHXrl2sWbOGxYsXs3LlSrZs2UJGRgbLli3D6/Vy33330atXL/r27cvcuXNr/XXVhZ5wjDGtgEsJhO0MAr3hVN4n/25577bf72M0MBoCn4gV+TO/zvYCMKj5oN3tTxz7xO4PeI7pMYYKfwUAJd4SDIELEz3848OkRafx7PHP8rcpf6NNXBuu6X4N83fMp3FE493DaEREROTQjRw5co/JFy666CIuuuiiPba59dZAJ9uyZb99Diw/Px+Ap59+endbRkYGEBhfPmnSJBo3bkxJSQmTJ0+usfr3p06EcGvtOuByY8xrQCoQWbkqEsgCzD7a9t7Hy8DLAOnp6Zomo6ZYC34v+CoC934vhMaAwwlFWVCa98f1sc0hMgnyt0LmCjCO324YiGwEiW3BWwbblwbO9u+3cYZAUrvA8XetDxzLHQ6uUHCHBR5Xo1+vAtonpc/utvuPvH/38tPHPb37h0GIMwSPw4O1lpt+uIlBaYO478j7uHHyjQxoOoAz2p3BkswlpEWn7Q79IiIiElxOp5Pzzz+fnj170r1796DMKV4nQvjv5AJTge7AT0A3AuPE2U+b/MrnhfJCKC+qvFUuJ7SG6MaQtRrWToaKEvCW/naf2h16nAeFO2HcmD3XVZRAeAJc8W3gGP/uATnr/3jsGxZBfEv49h5Y9O4f14/8L/S8ANZNgXHX/HF9t3PgtJegcAe8evwf18c0g/9bGlh+6xTI3bTnemcIXPtToIbvH4BV3wTCuTssENbdYdDnCmhxNGxdBCu/grD4wGsLr7yPbhL4Q+EAtIxpuXv5kQGBD3NYa3lh8AuEOkOp8FdQVFFEua+ccl85F319EZd2uZTrel7HTT/cxKltTuW4tONYl7eOFtEt9IFQERGRWnbXXXdx1113BbWGoIdwY8z9QDPgU+ArYDLwgDHmrMr2+wC7j7b6y9pAwLU+CIkK9ABnzIWywkB4LisI3BsH9A9MOs/EOwMBeI+gXQSXTIC4FjDhJlj49h+PNfwZSL8Uti6Er2/7rd3pCfQke8sCIdw4oCgzEFhDogK9065QiPzdyJ8+VwRqc7rA4QKHO3AfVvkJ5F4XQ8tj/7g+uXNgfZsT4NKJgAXr/+326zHCE+G8jwNtv9/GFfpbDSf9E0pyoKI48DWsKIGKot9qiEwJ9Lz/ur40L3BfkhtYv30JTP3nH79OPS8I/LGQsxFeH/pbOP/1FtcS+o8JbLvpx8DXKKYZhEYDgd7zX2dwAXh1yKsAVPgreG7Qc6RGpJJXlse2om3kl+eTVZLFyHEjuTX9Vs5qfxavLX2NoS2H7hHwRUREpOEyDfECJ+np6XbevHnVt0NrAz3DZYVQXlB5XwQpXSEkEjbODoTc8qI913c7E9oMDvRAf/33yoBduQ/rh04j4ay3AsM4nmj9x+OGxcHtGwLL754FBVvBEwmeiEAPrycSBt0d6Ole8z1krgys80RUbhcOie0gKiUQRMuLamwIR73i9wVCeXE2lOwK3EemQNPekLcFfngYiivbf73FpsHV0wPPfywtEO4BQmMD62LTYNQLgVC+bXHgj5rYtMBQnX0oqiji243f0iOpB0XeIs778jyeGfgMadFp3DzlZu478j7axbVjS+EWWse0xnk4ny8REQmq5cuX07Fjx2CXUSfs62thjJlvrU2v6r6C3hNeI/zeQI9tSFQgTO1YumfvcUUxRKVC51GBcPrVLb9bX1zZC21g9JTA/l4+NhCs9jZ6CjTuCcu/gB//G2hzhgSCuScSWgamzCMkBhp1AE/Ub+tCIiGp8iSGxsJF4/+43hP527HO/+jPX3ObQYHb/vw6PEMCf4BEJARue4tpAqOe/2P776c8OvcDKNgOeZsDQ2NyNwXGqnsiAusn3gkbZwSWQ2MqQ3pzOP4f0Kgj7FpPRFkBo5oNDpxnYNa5s3A5XGzI20CzqGbEh8bz49YfuWnKTbw99G3CXGFM2jiJ8zqcR3xo/O5x6yIiIlI/NcwQvn0JzHkFBtwMW+bDu2f8cZtWxwVCuHHC2h/27F2ObAQh0b9t2/vSwBCIkKjfeplDIiG+svd64O1wzC2B9U73H4/VtHegx3t/nC5oNfBQXrHUNMfvxm03P/LPtx36T8he81tAz90E2WuhcnYV5rwMP1YG/biWkNyZiOTO0Gkk7ZM789yg5wCIcEfwyNGP0DGhI1+t+4r/LfkfF3S8gLFrxvL60td546Q38Pq9GGNoFN6o+l+ziIiI1JiGGcJjmkHryg/4NekNl3z5W8D2hFcG7speS5cHbv7lz/eXfumfr9/PkAM5TKV0Cdz2p88V0Kxv4MOyO5bCjmWBD4smtAmMn1/6Gcz+L4nJnRmR3AWc8zi16UBOOu8kwlxhJIUl0TGhI/Gh8Tw570neX/E+M8+dybLsZZR5y+jfuL96ykVERCotWbKE+fPnc8kllwS7lD00zBAekQiNewSWw+MDs2KI1BUJrQO33ysvDgyBAnCFBIYOLf8cFry5e5OwY2+H4+5kQEw7BsT3haxVnNZmJJ0SOhHqCuWNpW+wPn89E06dwHvL38PlcHFW+7Nq8YWJiIjUDGstY8aMITU1le+++4527drxyiuvsGvXLhIS/vzK1i1btuT666+vUgjPzMwkKenAZk07WA0zhIvUN57w35Y7DAvcrA2MPd+xLNBj3rRy3vKNM+CTywBoFRJDq+b9YdtqHu9wCdsiArPETMuYRogzhLPan8UtU2+hXVw7RncbTam3lNDfzzYjIiJSDyxatIj169fzwgsvcOedd/Loo4/y6KOP0qtXL0466aQ/fW5kZOSfrt/b8uXLeeaZZ3jppZcOpeS/pBAuUlcZA9GpgVvbwb+1dxgBV88IfPZh04+wcRasmkhYnytpNexfULCdF0NaU96sL7aiFKdx4jAO/NbPkE+HcGa7M7mu53Usy1pGu/h2uB37+ByDiIjIfmy8cM+rVcaceiqxp51K1suvUDR9OhEDBpA4+kpyPxtL3tixe2zb/O238GZmsuXmvwHQ5KkncR1Aj3PTpk358ccfeeKJJ7j55pu5+OKLueiiiygoKKBLly4MGjSI8ePHM2/ePF599VWmTJnCzJkz+fnnn/cI4ePGjaOwsJDPP/+cRx99lClTpvDll1/Sp08fPvnkE6ZPn86UKVNYsmQJs2fPpn///tXwFds3XSVEpL5xeQLTY/Y4D055Fq6fB39bBUf/X2D9lgUw+SE8b47A/LM5/1y3jCuydlK+6SfO7XAuvRr1Iqski3O+PIe3lr2F1+9lyuYplPvKg/qyRERE9icpKYkpU6bw8ccf07t3b8rKymjRogXHHnssTZs2JTU1FYD09N9mCrzrrru45pprOO+88wDIzs7mnXfeweVy0aFDB+bPn0/r1oHhoXfccQdpaWksXryYjh07kpaWVqMBHNQTLtIwRP3uokodTobb1gd6yDfODNymPUFo8S6uruwpL53xb55pfyntU49izvY5XD/5ep4Z+AzdG3Xnl+xf6J/aH/e+ZvoREZHDXvO39z3jW+LoK0kcfeXux7GnBXrI9+ZKStrvPvanrKyMHj168OOPP3LHHXdw6qmn7hG495aZmUleXuCaHk5n4Foba9asoUWLFpxzzjm7t5syZQoejweAkJAQysrKqlTXoVBPuEhDFB4PHYfDSY/CVdMCF3065pbAuu1LCZ39PIMm3k/T/x5Jn28f4fmmIzgqqgXfbPiGa7+/li2FW1iVs4pZW2bh8/uC+lJERETeffddduzYgcPh4O6772bnzp04HA78ldfxcDqdlJSUUFpaCkB0dDSbN28mPz8fv9+PtZamTZvuHo7i9Xr54osv9nms3++3JqknXORwEBrz21SabQfD3zfB5jmwfiruFV8xYPp/oaSEs4Y+TtvwxrQoK+WBtR/x1fqvmHr2VOZsnYPDOOib2je4r0NERA5LiYmJHHPMMVx11VVs2bKFF198kZ07d/Liiy/Srl07RowYwW233cZZZ52Fw+EgPz+f++67j5EjR3LeeedRXl6O1+vlvPPOIz09nX79+vHUU0/x9ttvs2HDBrZt28aaNWtYtmwZZ5xxBkuWLOHzzz/nlFNOqbHXpMvWi0hgznKHE+JbwaL3YdzVlCW0Zk2ro+nc7UIuXfY8RRVFfDTiIyasm0DTyKb0aNQj2FWLiEgt0GXrf1Odl63XcBQRgcS2gQAOgQtdDXuSkJg0Os9/F/43mOeXzuLxRgPxWz9Pz3uaj1Z+BMAbS99gefbyIBYuIiJSP2k4iojsKSo5cFXPPldASQ6smkTYigm0SGgHxsEXncZQsHw8uxa9y3+W/Aef9dEmrg1jV49laMuhRHmigv0KRERE6jyFcBHZv7A46H524FYpvCSX8HXTYdl4prhCcTi/YU5hLg+ueoPk8GQ6J3ZmR/EOOid0DmLhIiJSnay1mF+v7HyYqu4h3BqOIiJV0+dyuGU1XDyBqN6XErF1MUd98wAfdLqGo5sczccrPuScCeewvWg7O4p2UFxRHOyKRUTkEISGhpKdnV3tIbQ+sdaSnZ1NaGj1XXVaPeEiUnVOF7QcELid+BCs/Z7OrY4Dh5Pz186lnS+OlFXfcUvuPJbuWsFXp31FibeECHdEsCsXEZEqatq0KRkZGWRmZga7lKAKDQ2ladOm1bY/hXAROTROF7QbsvthdNN+DJq7BMZfywWRsWxt0Q+TMZ8LFjxMz0a9uKf/PfitH4fRP+JEROoDt9tNy5Ytg11Gg6PfgiJSvfpdDdfNg0sn0qPNME5eOQ3vmyMY1XwoRzU+iqKSXZz82clMXD8x2JWKiIgEjXrCRaT6GQPN+wduJz2Ge9siLm55DFSUsv3ZrnRqlELj3C2s2bWSx+Y9wR1H3EHr2NbBrlpERKTWKISLSM0KjYaWxwSWK4pJ6XQ6T/38Aay9mVkJzciMjyG+rISftv3E1sKtDG89HLfDHdyaRUREapiGo4hI7QmPh6GPwd9Wwhmvc2RMG8atXkbct/fx1fqveOnnlzAYlmYtpaiiKNjVioiI1Bhdtl5EgitnI3hLsYntyFw+nri5r3KiexfdU4/gmeOeodRbSqir+qaEEhERqU4He9l6DUcRkeCKaw6AARrhgKy1/Ls8C3e+l+3hL3Da2re478j7ObHFicGtU0REpBppOIqI1B2dToEbF9Pt5P/Q0bph0l0cX5BPZ7+D+Tvmc+f0O8kuyQ52lSIiIodMIVxE6haXB7qfA1fPIOXcj3kotidNmh7JhrwNLNgyg4jiXczaMotFOxcFu1IREZGDpjHhIlI/VJTg/Vd7XOWFnN+qHb7IRrw/ajwb8zfSPLo5xphgVygiIochjQkXkYbNHYbrmpnw04u8suANMn2rycsfxlmOHZzf+SJu7HUj1lqFcRERqRc0HEVE6o/YZjDkYcJvWkbz4+4hImsddzlTGdFqBCuzlnPOhHNYl7su2FWKiIj8JfWEi0j9ExYLR92Iu+81jCzNg8gk5vz4DDZzOQkbZjMvORtroE9Kn2BXKiIisk8K4SJSf7k8EJkEwBFJ3fiwJBQzbgyvNmtJRkQs48/8joKKQmJDY4Nbp4iIyF40HEVEGobWx2OumQ2n/49nih08u3YJpa8M5JSxw3hx8YvBrk5ERGQPCuEi0nA4nND1DELHzKHV8P9iwuI5v+MFDGgygM0bpnLH9DvYXrQ92FWKiIhoOIqINEBOF3Q/h/Du53AVQM5GJn1yIjMSE/i/hL6sSe1MhDuS1MjUYFcqIiKHKfWEi0jDF5XKiQMf4ttdPhp9dDGPjj+Xy748D7/14/P7gl2diIgchhTCRaThc3kg/VJCb1gAJ/+Lh3YV8MD6ZXhnPsPpn5/OJ6s+CXaFIiJymNFwFBE5fLhC4IgrSe15Aanz3yCnxZG082+ncX4m2et+4PPiDZzb4VxCXaHBrlRERBo4hXAROfy4w6DfNcQBj6d0h1cG8WH+Cp5NjGdgRAviG/ciyhOFw+ifhSIiUjP0G0ZE5KLxnN3nJsZvz6PlW6dzx6cjGf31JVhrg12ZiIg0UArhIiIhkXDMraRdtwDb5wpO3raGk7euxm/9PPrTo6zLWxfsCkVEpIEJ+nAUY0w08CLQD5gGXAncAywAOgKPEfhj4d7ft1lr/UEpWEQarohEzMmPM7zf1VCcw6q8tXy+Zhy98neRcvRdFOMlMSwx2FWKiEgDEPQQDpwIXA74gPnAncAWa+1YY0wycCYQv4+2D4NVsIg0cPGtIB7aAV8nDyVmxtP8d903vBPu4cvTJ5IQriAuIiKHpi4MR/ncWltirS0HfiHwe29R5brFwDACveR7t4mI1LjYQfdizvuY4f4wrs3cTsK7Z/HU9zczYd2EYJcmIiL1WNBDeGX4xhgTCmQAiUBB5eoCIBlI2UfbHowxo40x84wx8zIzM2u8bhE5TBgD7U6kxVWzuPC4xyjP38b8tV+xeucSrLUszlwc7ApFRKQeCnoI/52zCYz7zgYiK9sigaz9tO3BWvuytTbdWpuelJRUC+WKyGHF4YSeF+C5YQHvnPAK1/b5G9M3fscFX13ADys/DXZ1IiJSz9SJEG6MGQZ8Za0tBL4Buleu6gZM2k+biEjtc4dhWg7A4/TQz2u4JzuPAR9fy3vjL+LeaXdQ7isPdoUiIlIPBD2EG2POJTA7yg/GmOVABJBmjDkLaAa8A7y9jzYRkaDytBnMmZdOw9VxOHnrfyBr+Tjcc19jdsYMSr2lwS5PRETqMNMQL0aRnp5u582bF+wyRORwsnUh/kn/IGvzbIY0T+OCThfyt/S/Ya3FGBPs6kREpIYYY+Zba9Or+ry6MEWhiEj917gnjosn0GjzT7zkNrSKbc28r2/imbKNPD74vzSObBzsCkVEpA4J+nAUEZEGwxhI68cRqX1J9PooXPklvp3LiJt0HwvWTWJ70fZgVygiInWEesJFRGpCVDIDr57HsdP+hf3pRe7ZNZ2EiBTePO0L/J5wHEZ9ICIihzP9FhARqSmhMZgTH8Rx3Txeju7FXeuWkDXraUaOG8lP234KdnUiIhJE6gkXEalpcc1pfNa7sHku68LCiZ+7hpSNP7Eucw27GrUnPaXKn+cREZF6TiFcRKS2NOtDK+DNIa/DSwO4w7+NaZFRfHvCG3hSuuJy6EeyiMjhQsNRRERqm8MBV07mnu7X82JmPp5Xjue8947hrYXPB7syERGpJQrhIiLB4Aoh7Oj/o+uY+ZSmX0q7vB00Wf41heWFfLrqU7x+b7ArFBGRGqT/fYqIBFN4PJHDnuKhrDFgDB+u+5KHfnqIztkZtEq/Co87NNgViohIDVAIFxGpCxLbAHBWfCu6LPmcDt/cw23L36IiuTNPnfymrropItLAaDiKiEgdYoyh8+lvY0e9RJfSUrqumgwfnM/rPz3BrtJdwS5PRESqiXrCRUTqGocD0+McLuo8Emb/lxU/Pcu/y5YQHduCU9qMwmLxOD3BrlJERA6BesJFROoqdxgccwsdrpnHuH4PMartaby76AVO+WAgOQXbg12diIgcAoVwEZG6LrIRLTqMxOlw0il3B8dlbyHufyfw/uTbWZq5JNjViYjIQVAIFxGpR44Y9DC3D3udkpAoXl3/OWO/uBw2/UROaU6wSxMRkSpQCBcRqW9aH0/YVdMZ3+1v3JCTx0/vj+TET05g/o75wa5MREQOkD6YKSJSHzmcRPa5ArqfS9qKzzm1ZD1d4jow6esbKEo7gpGdzsNh1M8iIlJXKYSLiNRnnghSu53LnQAbZjBx/ZfsyPiWUbm72NzpZJrGttYc4yIidZC6SUREGooWR/PkqeN4LrQ9WZPv5fTxo3hl0g3g9we7MhER2YtCuIhIA2JSuxJ34Thiz/2YG73hDJ37HisWvMqd0+8kuyQ72OWJiEglDUcREWmA3G0Gc36r42HVRMY5y5iz/iM8895gdXJ7klscQ7QnOtgliogc1hTCRUQaKocDOpzMKODkxkfj+m8/bolzEzsnnjeHfwCxacGuUETksKXhKCIihwFPRBKO6+fzWJOTuGnrJvKfS+e89wYwd8P3wS5NROSwpBAuInK4CIul48nP0POqn8jseDLlhTuJWjqWjIIMFuxYEOzqREQOKxqOIiJyuIlpQuvT3+Dj7cswUcnc//N/+XLNeL7teC0RPS7A5QoJdoUiIg2eesJFRA5TJqUzRCRyW5/beN40JnrCzVz6Vl9emHwLWBvs8kREGjSFcBGRw1yYK4z08z6n7LSXaO/10WTBe5S+OYxXpt9LQXlBsMsTEWmQFMJFRAQcDkK7ncM/LpvPKQPuZXbeGp5d9xkrsleQX55Pqbc02BWKiDQoxjbAfzl2S0iwX5w8DIDmb7+FNzOTLbfcCkCTfz2BKymJjZdc+tsTDMSMHEnsqFFk/+9/FM2cRcRRR5Fw+WXkjR9P3udfwK+XfTaGtFdexpuVxda77gKg8cMP40pMZPPV1+zeBmOIPvlkYoYPY9dbb1M05yci+vYj/sILyP/6a/K/nrh7Oww0eeopfDk57Hj4EQCS77wDV0ICW267rXKXBoyDqMGDiBo8mNxPP6Vk0SLCevQk9vTTKPjhBwqnTgVjKi9RbUi++x/48/LIfP55jDEkXnMNzthYdv7rX2AcgWM7DBH9+hHRrx8F339P6YoVhHbsSNTxx1M8fz7FCxZgHI7A9g5D/MUX4y8qJu/z8RiHg+jhw3FGRpL72VhwmMC2DichbdsS2r4dJUuX4d2xHXdqKqGdOlG+aRMVGRngcGJcTnA4CevZAyoqKFu7FpxOQlq0wHg8lGdkBPbndGKcThzh4TjCw/GXlwe+Ji5XYL2IVL+yQjbuWETztKN56PsbmbFlJuOHvkNIUodgVyYiUqcYY+Zba9Or+ryG/cHMyj8wrLVYn3f3MoCtqNhjG7yV68vL8RcX4y8L9Pr4y8vxFVb+O9b+bp8+P77sXbuXAbw7d2Kxge0Af+XzvLuyqdi0GW+bNpWPd1G2bu1v+7MWYwy2vJzSpUsD+/R6sdZSsnDR7m2s9RPSoT0AZatWUTh1GiYsDIDydesomPTt7m2xlpR77sZfVETeZ2PBWuIvvhhHTAy73nq7cn+Vx3a5AyF80rfkjR9PzKhRRB1/PEUzZ5H1/PN7fEkTLrkEX24OOx54EIDIAQNwRESw7c4799gu8dprCW3fjpy33w7s89RTafzoI+SNG/+HfXZY/gsVO3ey/tTTAGj93Xe4mzRm7eAT/rDPpOuvY/s995I3bhwxo0bR+LFHyXrhBbJeeTUQyitvbaZOwbtjB5suuxzjctHslVdwNUpi4wUXBrZxuzEeDzEjhhM9dCi5n42ldOkSwnr2JGbECIrmzKFkwcLd2xmPh9izzsSWlFA4YwaOkBDCjzgCR1gYpStX4QjxYEJDcYSG4ggPx3g8+/6eFKkvQiJpnnY0ACd5UmixK5uQF47iwXZ9OLrnlRzX/vQgFygiUr81yJ7w9PR0O2/evGCXUe9Ya8Ef+IPCOJ3Yigqszwd+P9Zvwe/DGR2N9fnw5eRg/X5c8fHgdFKxZStYP/h8WL/FGRuDKz6eii1b8OXl4YiOwdO0CRVbt1KxbRvW6wO/D+v1ETngaPzFxRTOnAk+H5HHHosJDSVv3Pjd2+D3EdqlC2Fdu1IwZQplK1YS0q4dUccfR+H0GRTNnBn4w8VbAT4/qQ8+gDczk+0PP4L1VpD64IM4o6PZdNnlge0qKrDl5cSdcw5x55zN9gceJP+rr4geNoyUu//BzmeeIfvFl3774jiddFy2lPKNG1k75CQA2vwwGVdSEiu6dN3j65h4/XUkXXst2+6+m4JvvyNm1CiS/347Oe+/T9648ZjQUExoCM6ICBo/+ST+vDyy33wTZ0QEseecgyMigsLJkwM9/xEROCIicCUlBb72lX+widS6gu3kT3mUi7d/w6iiUs7veB7z2w/iiObH63tSRA5rB9sTrhAusg/WWqiowF9ega0ox5ZX4E5uhL+8nPJ167BlZYR27AhOJwXffY8tK8VfUootKyWse3fCuncnb/x4Shb/TFiP7sSccgq5n40lf8IE/GVl2JISrLW0GvsZZWvXsm74CLCWtjNn4AgPZ2XPXnvUk3TzzSSOvpItN99MwQ9TiDvnHJJvv42c99+n4NvvcMRE44yJwRkXR6Mbb8RXWEjRrFk4Y2IJ79kD3G5scTEmPFyBSQ6Jb9da/FP+ycTNk7kzNozXhrxG76SeOJwN+x+rIiL7oxD+OwrhUt9Yvx9bUhIYXmQtpStW4C8qCgyNKioitF07Qtq2Je+LCZT+8gth3bsTfdIQdr37Lvmff4EvLw9ffj4YQ7uZMyhZtowNp58BQLs5PwGw6oi+4HbjjI7GGR1N4pgxxIwYTs4HH1CxZSsR/fsRceSRlGdkYEtLcSYk4IyNVWiXfaooyuLbHXMYmnokz749kLXxzXhqxPu4QiKDXZqISK3SmHCResw4HJiIiN2Pwzp33ud2MSOGEzNi+O7H8eefT/z55+9+/Osf1SGtW9Ny7Gf48vJxREZiS0podMvf8OXl48vPx5eXhzM2FoCiWbMpmDwZXE4ijjyS7JdeIvfjTwI7dLsJad2aVuPGUrZ+Pdkvv4IrMYGkG2/E+nyULFyIKykJd0oKjt/VLw2fOyKRk1udDDkbiQ2JJWnbElz/PYIPuwzhqL430TSmebBLFBGp09QTLiJYvx+8XozHQ+nKlZStWYMvOxtvVjY4HTS68UaK581jyy234i8qot2cnyhfv551lbMQAThjYkh9+CGiBg9m1zvvYsvKiDj6KELbt8dfVIQJC9NsNg3Z+mlkfXcvQ107uajcxbUn/JtdqV1IDEsMdmUiIjVKPeEictCMwwGVM7qEtm9PaPv2f9gmPD2dtlN+2P3hUHdKCmlvvIE3cycV27ZTsW0r7iZNAMj97FPKfllOclgooe3bs+Xmv1E0axau1FTcqamE9epJoxtvpGLbNsrWrcPTrBmetLRafc1SzVoeQ+IVk5mw5AMiZjzL5Jzl/H3WLbx57DN0bnIk6A8wEZE9KISLSJX8OkbcER5ORL+++9ym1Wef4SsoCMwvD8SMGklIu7ZUbAnMjuPdug2AwqnT2H7ffbgbN6bN5O8pWbaMrBdeIKRFCzwtW+Jp2ZLQTp1whIbWzouTQ2MMyd3Oha7n0L4wg/NsMe2/fZB3vFlsTevN3wb/F6fLHewqRUTqBIVwEakRzqio3cvRQ4cSPXToH7aJGnIinlYtsWVlAPjz8ijfsIGiqdN2z+Xf6ovP8bRpw+bLL8fVKJmEK68gpHVryjMycCUl4QgJqZ0XJAfOGJpFNePm3jdDSHO2/vgQm9ZPxvnSACZ2PZn03teQGJEU7CpFRILqgMeEG2OOAfzW2hnGmFjgCSAKuNdau/KQCzGmK/CLtdZ3qPvSmHCR+s16vVRs3Ur5+vWE9+8PXi+bx1xL+fr1NHvxBUI7dmTVkUfhy8nB3bgxnpYtAx9aHTkyMC99eDjGrR7XOsPvw7fsMwqmPs6giBLOcsRw20UzyC/PJyYkJtjViYgckhqfotAYMwW4wFqbYYyZADQG7gcGWWtvqOqB99p3P+B7IAHwAvcCC4COwGOAY+82a61/f/tTCBdp2Ky15H/1FeXrN1C+fj1l69cRM3wECZddytY77iR/wgTiLryQ5NtupXTlSvwFBYR06IAzUtPnBZXfx4YFrxJp3Kxr3IXrvr+Wl9ucR48jbgCHM9jViYgclNr4YOaLlQH8YmAQ0M1au9oYE1/Vg+7NWvujMSaz8uGVwBZr7VhjTDJwJhC/j7YPD/W4IlI/GWOIGTZsn+uiTx6KMz6O0A4dAdj19tvkffIpAO5mzQjr2YMmjz+Ov6gIX34+rpQUzYVeWxxOWqRfBUBJ/mZOiWhBx2/uY/zid1jXvA/XDXoGt1vj/0Xk8FCVj6v3Msb8G3geuLEygLcBrq3mmvoBiyqXFwPD9tO2B2PMaGPMPGPMvMzMzL1Xi8hhInLAAJJvvXX3fOqNbryRZi+9SNJNNxHapfPuseZFs2ez5rjjWTPwOKzfjy8vj/yvvqJi27Zgln/YaBbdjH+M/JCQ019njcPPwo3f43r5GKbNfJSc4qxglyciUuOqMhzFAwwFNlprFxljmhDoETfW2jcPuRBjNgAdgPHAzdbaZcaYLsCTlZvs0WatHbK/fWk4ioj8lYotWyiYOhV/Xh6J11xDwfffk3HtdQC4UlII69GDRrf8DU/TplifD+PUcIka4/dTsfQTyqf/i+PCixiaNpj7B/2b4opiwt3hwa5ORORP1cZwlKOsteN/fWCt3WKMmUigl7o6ZQO/DtyMBLIAs482EZGD5m7ShPjzztv9OPKYY2jx8ceULFoUuC1ciCM8HH9ZGauPOpqQdu1IfeB+Qtq0CVxxNEYfKKw2Dgfubmfh7nIG760YS3haf5ZlLuXyr87n2VZnc0T/W8DlCXaVIiLV6i9DuDGmceV2Q40xawgE4l8lEfjg5OfVWNM3QHfgJ6AbMKmyfV9tIiLVwrjdhHXtQljXLnDhBbvbfXl5xJ55JiULFuCMi8NfXs7qowfgatSIsJ49CevRg8hjj8HTrFkQq28gHA7adDodAG/BNgZ7nXSc/DjfLH6H2U06c+sJzxERmRzkIkVEqsdfDkcxxpwIvEZgNpS9FQHvWWuvOqQijEkHpgLnAhOAB4Cfga7AfYDdu+3PpjLUcBQRqSn+oiJyPvxod2+5NzOTlAcfIO7MM8l89j/gdBA9ZAghbdoEu9T6z1pY8x1vzHiAryt28P6uMr5NP5umPS6mc2LnYFcnIgLU8BSFleO/+1prPzuY4mqbQriI1AZrLd6tW3FERuKMiWHTFVdSNHMmKfffR9xZZ5H14ksYt4vwvv0I7dhB48oPgW/zHMyMZxhmN9E2pRfPpv+dVTmraJt2rGa3EZGgqvF5wv/kwMdYa6cd0k6qmUK4iASLLzcXXC6ckZFsuOACSubNB8ARE0PCJReTeM01+IuLMWFhCo8HobC8kMKKQsq+u5dTcmZya0hzzh/4KP7kzjg117iIBMHBhvADnqLQGHOyMWaaMWaNMWZd5W09MLGqBxURaaicsbG7LwrU4p13aDNtKo2feIKoEwbjSgpcqn3Ho4+xesAxZL3yCgD+8vKg1VvfRHoiSYlIIeWYO7g7tgcnbVzMjLdOYNTbfdj4y6eBISwiIvVAVWZHeQ14CFhKYIz2r88/p7qLEhFpKNyNGhEzYvjuecsBIgYcjb+0FGdUNAA7HnyIolmzCO/Xl4h+/Yk46khc8Yd8HbQGLSSuBWeMegdKcgid9jDNN35F44+vZNxpBeQ5DBd2uhCHqcqlMEREaldV5gn/wlo7Yh/t8dbaXdVe2SHQcBQRqU/yJnxJwTffUDRnDv68PEI7d6blp59QvnEj3l27COvWTePJ/0pFCWz6kTu3fsu2wq28nl3AD0070y19DAmRKcGuTkQasBofE26MuQhIA/Ye/z3SWvu3qh64JimEi0h9ZP1+Spctw19UTES/vux44gl2/e81wvv2pfmbb1CxcyfG4cCVmBjsUuu04qzV+D65hONDchlVZrmrw0WsbXs8rZscEezSRKQBqo0QPgtoAZT9rtkBJFtrQ6t64JqkEC4iDYE3J4fi2bPB6SJ6yIm7Q3lop05EHDOAyAEDCOvVSx/w3BdrWbfkPcIWf0RmxizOb5zCE0kDGDTk32DA7XAHu0IRaSBqI4QPsdZ+s4/2gdbaKVU9cE1SCBeRhqhszRoKvvuewhnTKVm4CHfjxrSe9A0VGzdSPH8+EUcPwJ3cKNhl1jkF239m/KyHObXtGUwKdfGf+U/xbstzSe11KbjDgl2eiNRztTJFoTGmFdDMWjvVGNOz8vkLqnrQmqYQLiINnS8/n4otWwjt2JHs115n5+OP427WjDbfTqJi+3Yqtm4lrHt3jSXfy4IdC/hy2n38Y9lU/pOUwo6kNjx0wvOYuObBLk1E6qmDDeEHPDuKMeYa4Dnge2CqtXahMeZuY0xra+3HVT2wiIgcPGd0NM7owOwq8ZdeQsRRR+LNzAIgb/znZD79NM7YWCKOGUDUwIFEnXACxq0hGL2Se9HrjPGQPh3nzPtx7/wF82wPnmzdgx69RjOo07nBLlFEDhNVmaLwAuAIYMjv2l4CZgIK4SIiQWKMIbR9e2jfHoC4c8/Bk9aMwilTKJw2naIZM4kaMoSK7dvJ//JLIgcOxNOq1eE7ltwYaHkM17b8HnI3UzLnZaZtHkt40TYG+n28Ofthhne+kEaxLYNdqYg0YFUZE/6ItfZOY8xt1trHK9uGAO9ZaxNqssiq0nAUEZEA6/NRsWULnrQ08r74gq233oYzNpa2M2fgy8mhdOVKwvv0weHxBLvUoPL7vFTgY2XmUi6YeDFP5BTTt90o1rYdSK+2Iw7fP1hE5C/V+HAUINMYcy6QYIxpBwwkcPEe9YKLiNRRxunEk5YGQMyIEYT37k35xo0Yp5P8SZPY8cCDOMLDiTjqKCIHHkv0sGE4QuvUhFe1wuF0EYKLbsm9+PLIf5Ly86e8s/pTnsr6lq9mv4Cz82l4Op9KYpimhxSR6lHVD2aeBVwKNAeygc+Bf1tr69Q1l9UTLiLy1/wlJRT9+COFU6dSOGUqvrw82v04G39xMTnvvkfksccQ2rkzxnF4XnmyOGcDs2b/i8GrpnFXXCRTPIYpZ05m+9pJJLc+EY8rJNglikgdUCuzo+znwI2ttVsPaSfVTCFcRKRqrLVUbNmKp2kTCqdOZfPV12BCQmj304/Y0lKKZv9IxFFH4oyKCnaptc/vZ93Oxawty+IEfyjnfHs54Y4QXmtzPtvbDSI5NV3DVUQOYzUSwo0x/YDV1tpsY0x/oO1emziAYdbaM6t64JqkEC4icmi8OTmUrVxJRL9+5E34kq233AIuF+G9ehF57LHEnnnG7tlZDie2rJBZc57FrptMnw3zOK5ZE85wxHDzkfeRm3YEsaGxwS5RRGpZTYXwBcCz1to3jDEXEhgDvu53mziA9tbalKoeuCYphIuIVB/r9VKyeDGFU6ZSOHUqZevW0W7WTIzbzc4nnyLymAFE9OuHOcw+3FmWvZbPZ/+TDhvnENP9fE5Z9y6P9fw/Boc3w5fWnxBdCEjksFBTIXwUkGGtnWeMiQB6WGtn7rXN8dbayVU9cE1SCBcRqTne7GxcCQmULFnCxgsvwpaX0272LExICHnjxhF5zDG4GzcOdpm1x1oyC7fz/uqPOXv7BpYtfY9/JCXyZupJtOh9Ba7E9hquItKA1VQI3wBcXHmFzKOttTP2sY3TWuur6oFrkkK4iEjt8JeWUvrLcsJ79aRo1iw2XXY5ACFt2xJx9NHEX3wR7pQ69c/SmlVRwvKF/+PjFR9w5/plvBYdyVdxiXx04v/wJnUgzBWmQC7SwBxsCP+rj7w/YK2dWrl8zH62GVHVg4qISMPgCA0lvFdPAML796fVV1/S6LbbcMbHk/POO2At1utl87XXkf2/1/Du2hXkimuYO4yOR1zHPRfNwPV/v9Cq4+n08Ro8EY24d9a9XPTxEJj6BDs2zcTvr1P9VyJSy/6qJ/wSAlMS+ghMS7hxr02cQBddrEdERPbmLy3FERpKxbZtbB49mrLVa2gz+Xtcyclsu/dewtPTiTjySNyNGgW71FrxxdovKJjzEuetnM6pTVJIMyH8O20ky5t2o03bU3C7NeWhSH1UY1MUGmPaAunA6cAXe612AiOstadW9cA1SSFcRKTuqdi5E3ejRpRv2sSGc87FV9krHtKuHcn/uIuII47A+v0Nfl5ym7eViXOfIWLrIvpsXMAxTZM5o/VIbhvwMNNWfEyfFicSHhYb7DJF5ADV1Jjwh4FF1tqPjTHHWGun7WObNtbaNVU9cE1SCBcRqdus30/ZihUUzpxJ0cxZJN95ByFt27Ju6Mm4Gzcm6f9uIqxr12CXWeMqirKY9fObNO5wCg7jYNT4Ufwjt4ThTY5hUlJTju91NTFRh9GHXEXqoZq6bP0JwEuVy832s01uVQ8qIiKHN+NwENqpE6GdOpF45ZUA+MvKiDz2WIpmzcR4PFhrWT/qVNypqYT3SSc8PZ3QTp0wbneQq68+7ohEju3/NwC8vgpe63gVbTJ+5sdNk7mnKIxmc14joXEfZnU7hVPan0GU5zC8WJJIA/VXIfw1YHPlcvO9V5rAR7zPB/5dzXWJiMhhxhESQvIdf9/92F9WRli3bhTPm0fhlCkAJF57LUnXX0fh9OkYTwhh3bvhCA0NUsXVy+V00+eI6+AION5bwQe/fESHjJ95L2sOjy94ipPbjuKbd4byg9PL3Z0uw9PqONxRh9HMMyINzF8NR+kPvM/+e8ENYK21zhqo7aBpOIqISMPizcqieN58Qtq1JaRVK9adehply5eTOGYMSTdcT8HkHzAuJ2E9e+KMani9xduLtpMSEs/77w/jk7IMPsnYylNxsUyLjuWzlKFs6ns5UWFxJIYlBrtUkcNOTX4w0wCpwP8Bz+212gVcZK29t6oHrkkK4SIiDZsvP5/iBQvwNG9OSMuWrD/jTEqXLgWHg5AO7YkZNpyEyy/D+nwYZ53qJzp0Pi9sW8Q3S95k5c5F3FDs59p2PdiUv4kv/Ml87bZEN+7FUd0uBU94sKsVafBqLIT/7gDNrbV7T1GIMSbOWptT1QPXJIVwEZHDi7+khJLFiymeO4/iefMI7dSJ5NtvI/M/z5H72WdEnzyU5FtvxZsT+HXliosLcsXVyFqW71rBrqIdHPXdPznVt55mFRU8m5XHfc3a0DGhE2cPexmvAZfjr0ahikhV1dQHM38v0xhzH+Cx1t5pjOkJDAGerOpBRUREqpMjLIyIfv2I6Ndvj/bQTh0J39ALR1igRzjnvffI+s9zuJs2JbRrF8K6dCH+4osxrnocTo2hY0JHSOgIlw3kw+Jd5G34AX/GIjK2TCBx+3z8DgcnfDyIMyvcjIntxudhLrq3PJHmzY8FRwP7T4FIPVGVnvCxQBsCUxZeWNk2CDjLWntVzZVYdeoJFxGRfSldsYKiGTMoWbqM0qVLseXltJ02ldLly9ly0/8R2qULqQ8/hHG7saWlOCIigl3yofOWU2y9/O/nV+ix5HO6bF/JMalx3Lwrh3OL/VzeJJXLj32EgS1OYPXWObRO7oXL5Ql21SL1Rm30hJdba7saY27/XdsvwJlAnQrhIiJy+Cj3+skrqai8lZNbHFjOLa6g1OvD5TC4HA5cToPTEYa7+xCcPU/C5TS4y8tYu3Q7YRuziU5pRvHKNWzKKCRk01pCr70U0lrgbNeO0DZtSDptFOHNmmKtJfBxqXrC5SEcD9f3vhF634j1eZmYMZvQzFXkbl9MaM5c3O5wNuZv5IzvruSBnGKOiWvPqxEezmxxMi1aDYbY5jjUYy5SraoSwtdW3v++63wMsKv6yhERkcOd1+dnXVYRa3cWklNcQW5JeSBgF1f8FrBLKsgrDrQXlfuq58CpowLTELz6E0nFOZzQ/gTa5WymxYw5JH/7DaN+cbIluSUvfPUQXk8Ik44/n9z23Wm7Yy2e+FicaWlER4YRE+YmNtxNTFjgFhvmITrMVWeCu3G6aNJ8ADQfAMD/Ktvzy/N5tNkI0mNz2bhzMZ/48zjh23+wkH9wXVpLXjjhJRptmsfM3JUMbnUyMak9MKGxdeZ1idQ3VQnhXxlj3gWSjTGxwHFATwLzhIuIiFRZSbmP5dvz+WVrPsu25vPL1jxWbC+gzOvfYzuP00FMuJvYymDbJDaUTqnRuwPvHqE33FMZft2Eup34rMXr8+P1W3x+S4XPj89v8fotXp/F6/dXttvK9sr1vhOp8PnJLfexOSefEV5DTomXrVuPJHL7Zoo9YazLKuT8958hsSgHr3GwJTKJb9PS+bTtcaQWZhHiq2BLZBJ43CREhJAY5SExMmT3LSkqhMRID0mRISRGBdpiw9w4HLUfbKM90Qw//hEAUoDZ5UWYzBWs2zSdYaaQplFNmb3oGh5wF5L+02vMcbt5MCmRd00TSgf8jdklWzm1UR/CnGE4Y5rhdDWciyqJ1IQDHhMOYIyJAIYDaQR6wL+x1mbUUG0HTWPCRUTqnpyicpZtzWfZ1rxA4N6Wz7rMQvyVv4aiQ110bhxD58bRdGocTbvkKBIiPcSGeQh1O+psj2vJsmWUr11L0arVFK9ag7dbTwqGnwHPPEHkxHHs6nYEMy67g/A5M2m05Ce2hcWx0RPLWmc0S2PT8BvHHvtzOQwJkXuG9cSoQFBPigrcGkWFkBQZWus97H5vGdu2zie5KJtftsxmXOY8binyMa73GTz68/PMiD6Sr9dN4ImEOL4pi2V9bGPmhLi5tO/t+BJa4jROwt2aNlEalhqforDyIP2BK4DGwDrgv9baX6p60JqmEC4iElxbc0tYsiVvd+/2sq35bMsr3b0+NSa0MmxXhu7UaJrGhdXZoH0wyjdupGTpUpwxsUQefRS73n6HrJdfwpeZFdjA5SJl9hyyNmRQ9n/XU5rYiOWX3Mw2G0LEnBlsDollvSuazRUusgrLKff5/3AMj9MR6EmP+i2gJ+3uYQ/Zoz3cU7MzwOSV5RGzawOLV33BDzvncWOJ4bWiNbwY6md2v0d5pWwLLy1+gbnZPr6KjWd6iIN/NhrIypAQdsQ2ZmDXi/H6K3A53A3q+0AavtqYJ/xs4FXgK2ADEAIMAB621n5W1QPXJIVwEZHat3lXMV8u2caEn7eydEs+AA4DrZIidwftzo1j6NQ4mviIw3f2DX9pKRVbt+LdmUlEv76Ub97MzsefoGLLFpp/8D7+/HxWHz1g9/aOqCiS77wDM2Q42996m6JtO8jq0JPNzdpTsGkLOYWlbCKM7SV+MgvKyC4qY1+/2sM9ThIiPSREhJAQ4QksR/62HF/ZnhgZQnyEB4/L8cedHISKklzcrlAW565k0bKPuHhXFm/l/cI3/lze3bKVR2KjmBCXwKwL5/P49zfx1cZv+aEshnFRUaxwu7ijyQmsjEqgOLULPRv1bJgXYJJ6rTZC+KfA1dbazN+1GeAxa+3t+39m7VMIFxGpHdvzSncH74WbcgHo3jSGYd1SSW8RT8eUaMI8CkxVYSsqKFu9mvKMDCoytlCRkUH08OGE9+rJ5quupnD6dJJuuJ7Eq69my223kf/5FwA4Y2II7dyZxq+8Quby1eR88imFkbFsGjiCrJwiijdnsIMQtvpdZBd5yS4qI7uwHK9/3zkgKtS1O5AHgnoI8RGBD5r+Oj4/NtxDXLibmMox+SGuKp5rv4/c7NXsrMinXeMjmLLkHVat+JTRFSE8U7yGObaI97Zs5a7W3ZgbFsakEZ9y/yvdWRYWzkcVsbwV7ibD5eLO5GOY2eF4CsoLOMmVwLbyPExkEikJHUHTLUoNq40pCn/4fQAHsNZaY0xRVQ8qIiL1V2ZBGV8v3caExduYu3EX1kKn1GhuO6k9w7s2Ji1BY34PhXG7Ce3UidBOnf6wrtlLL2J9PqzXC0D8+ecTccQRVOzciTczE0dYOC6ng8isbeR++gExYWGcccsYSlesYP3111cewOCMjqbxE48TMWAQmx96lJLsXRQNHMLONl0p/2k2Jdm5ZDtD2UkIW0vD2FgawYJNOeQUV+DbT2iHQG97bJibmHBPZUj/9fbb46hQN1GhLiJDXJXLLWga48Jay8CuFzCw6wUA3PTrTkvzGZO7nl1OB/h99EwbSErJTvBFkFW6ni1lJfDLeD4yWWwq2MRJG9bzhLuAtW4347ds457kFHZ6Qnjx9Am8vXUqBRlzGONpwjRvLhXuUAYlH8E6fwnEtaRVai/KKorxOEMxjur5T4DI/lQlhDc1xrSy1q4DMMY0Bi4HutVIZSIiUmfkFJUzcdl2Jvy8ldlrs/FbaNsokpsGtWN491RaJ0UGu8TDhnE6dw/HCOvenbDu3f+wTdTAgXT4eTH+ggIAXMnJNP7nY/jy8vDl5uLLzcWdmooxBrNtC84VK2g18Gh6dUlh01NfUDRr9u59hXbrRsuPPiR/0iS2P/AIzsZN8bz4GnkrVuF96b+UecIo9YRS7PSwosNxbIlIJGHRjxSXVbAiugnznFE4sjMp90OJK4Qypxtr/hhwHYbfBXPXHkE9svLx1JBMIhPuJi7ExQSPkx4eJ/3dLhZ7nFzprMBvSsnrvIVzMudQXLIT2kXTIXMuKWW5EBLF6pzVZGcuhtVv8W5yEgUOB4N+eJrHk5PIS2zN+2d+w/99fg452St5P7eC++IiKXW6eczVjP8lJlPepAfXtD2bb6beC+4whiT2ZGFZFsYdSo+UdDKiknAaJ6nGQ5nD4A6J0fzqsl9VGY7SDJhA4EOZYZW3ecAoa+22Gqvwt+O7gHuBBUBHAsNg/vgpFTQcRUSkOuSXVjBp2Q4m/LyVGauz8PotLRMjGN4tleHdGtM+JSrYJUoN8O7ahTcrC39eHt7cXByhoUQOGEDxwoXkjR2HCQ0h5c47KVmyhG1334O/sBB/YSG+wkKav/4a4X36sPq44/Fu20bqo48Se+ooNlxwISW/+73s7XUEWfc8gX/q98R/8DpFiSn8eMVdODesocPEDyhxuClxuCk2biZ1OJb1nlj6Lp8BXi8Lk9qyNTKJzlnrCPeWUe50UeFwkeeJYEtUI8IrSomoKKHC5cEbFUO42xDucRLqcRPucRLucRHhhgiTTaSjgGTjoMiuJT88haToo8jNfZ+4omUM9cXwmV2Lz5ZxbYmTuxOiKIhpwR1Nz+WR2ZcA8Mb2nVyW0gg/8Jovgctbd8Jvfbz581SuiHFTYQxvZhdxc0IMxuHiyQum8tiSlzBbFnB7eQiv+nfhcLq4LKoDH5dtxdmsH6f1vpZvF/0Px661DIrvytySbThdofSK78Aqh8UV15JWEY3ZmbkEpzuChPBkSqwPpysUjycS1INf62pkTLgx5qnKxQLgbQIzovQFmgIbgXRr7fNVL7fqjDHXEBgB86Ix5mogx1r74b62VQgXETk4Pr/lm2XbGbtwC1NXZlLu89MkNozh3VMZ0a0xnRtHa+YK2adf84QxhvKMDPz5+bhSU3HFxVE4bRoVW7cGAntxCa7UFOLOPJOiH38k58MPcUbHkHr/fRQvXMj2++7HX1qCLSnFX1pK2isvE9a9O6sGDMCXmUX0w49hjz+Bgssvhl+W7j5+cbferL31USInfU7Lt5+jKDGVr25/jthf5nPC64/gczjxOt14nS5eHnodaxKac+Pn/yK8rIi3up3Cj43ac9X8j2lWsBOvw4nP4WRVbDPe6TiE7pmrGbxpHllhMbzZ6WRaFm5i+IaZOI0D4yzBOPzMb9aLGQlRDFu1hL7lWfzS1ktxnINBK31scWThdPjZaEeSkfgLnsbbuGNTJp94ffjcfq4LK+DWqAjcOak0Dr2SjPLniPdlc48/k0vTkmiyA+7JyuGyDm3IjmvJ6JITmbnjEZzAI1nZ3NQkgfJweDQvjhvj0zDuEF7ZPo/boh1ElsGdRQ4ei3LgNg4GHvMpH21/nUY7FnNjoeWl0Dw8DjeXkMybJovy1H4M63wrk5Y8TFTOas4IaclY7xbcDhcjwtrweagD06grJyT0ZvbCf+F2uBkY1Y5pxRvwOFz0b3QEM6OicTtd9M/ZyYLC9bicLnpGt+bnogxcThddul3EytzVuPI20d4Tx5qibTgcLlpFNWVD8Q6csWk0S+xExs6lOEpzaRyewvbSbJwOF0lhiWThxRkWT5w7kry8jRjjIjokmiJvCQZDeEg0pc7A9J0hfj8VPi84HLidHgK9tybwH4pq+llWU2PCbwI+Aa6w1uZXtu3+H5UxJsIYc5619r2qHvgg9ANeqFxeDFwD7DOEi4hI1fj9lglLtvHs96tZs7OQlOhQLuzfnOHdUunRTFdFlL/2++8RT9Ome6yLPOaYfT4nol8/Ivr12/04vGdPWo0ft89tW389EVtagiMiAkdYGGX/ehx/QT7+sjJseQXOqEh6d29GWcJQStonY8LCuGdYJ8q7RZAXnoctLw/cKsp57rJBeJo2ZVvBFHy5ubx00TGE9+7N1gfmU7qyAn+FF39FBf06xnLhDcdQPrEAx+tb8cb6GXBROo75PpKnLwO/H/x+HD4viadcTp+0thz9xXuEFORR3u1m1rftT/TEO2idkQfAUbzD+lbdeL/lo3yS/x3DJr/DzthUzj7/FdqsX8JVX/0HeLjyFbu4ccgd5GS5uf6zt9lQ6iK1qBtz0noR+vU7jM4NRLi1JDO8SSQTBjfji8VO7l60lM0xUbx7Qi+ab9/A8Im5bMHPhZXx89alX7G6YybvvrWeHSV+Qoc42NTakPnBdobuAvgUP5/RvLmbqaeUMGLWAvouDGNXPEQP+pK55a25bMKH7LAuWjnKA5UOfIv3Oscz5g0Hm4o+5f0TkvmpVTxff7wYT74bA2zw+1nfwvDtST7Mkxu5YNVY8mMt7Y/O4DVvMqdMcuD3+djpdFKBiyuOuInyrq/xj/ezKS708/Ygw7aWfu751MuawggqcBHqi2BzWjZTBvv415wCFq6MozDa0qJfJPf5Y7n8+2zaVBSR4XbiAI5Iz+Tyjglc9InBFDXlneOgrFkuT47LZnVxKAZoVuFldnMnrx/Xmh4/D+bEpa9THlXBcel5PO6L5vhphqYVftZ6QqgghNlHnnngb5K9/FUI3wxcYK0t39dKa+0PxpjngdoI4SkEeuSpvE/+/UpjzGhgNEB8fDzp6YE/SK6//no6duzImDFjABgwYABPPPEE/Srf9BEREUydOpVLLrmEpUsDf1G///77fP3117z11lsA3HrrraSmpnLzzTcDcMIJJ3DXXXcxcOBAABITE5k4cSLnnHMOa9asAWDs2LG8//77fPTRRwDcc889hIaGcueddwIwYsQIxowZw9ChQwFo2rQp48aNY9SoUWRkBK5/9PXXX/P888/zxReBT74/8sgjlJaW8sADDwBw1llnce6553LqqacC0KZNGz744ANOOukksrIC89BOmTKFhx9+mG+//RaAp556im3btvHEE08AcNFFFzF06FDOPfdcALp06cIbb7zBscceS1FR4DO3P/74I7feeivTp08H4Pnnn2f58uX85z//AWD06NH079+fSy+9FIA+ffrwwgsv0LdvX3w+H06nk59++olrrrmGuXPnAvD6668ze/ZsXn75ZZ0nnSedpyCfp7ySChqdchvL507Du3IKydGh3PiPO2gS7ebK0y7Seaoj5wn0fqryedqyJXCe3nzjr8/TVVf98Txt3sj17VvTsVNHxhTlw/p8BrzyWOA8/fsBwLn7PP2z8jy9YS3vfPAB+ZO/56sHL+FLn4+b/+8mUhMTufX++2HLCk5cM44r7hjDoGnvQ2EmSV8+zJMffsg5S8exdvPmwLl+5BGaLl3Kj2M/5qrSEm4//3wGt23LtH/9i3sKCzmhVy8uPuFEzn3oIcgooMlML/945CEuueQitmXtwkzw8r9nX+D5NY/ww8+LAcsNJ5/CCR1CWPfaXC7KKeSELt04vv9VvPTwP5iYV0iz6BgeOv1cbnznNXKWl+DPiGbIHU/x4dp/Mmf9RnwfN+fy8y/h82aT+WD+j1jr58R2HejY7lZmPfk4c3NzaB0Xz5ndxjDjqccYsqMIvBU8d9JgnliwhPk/ZlKxJJzLTg/ns42xTFi8nBvWx3NU7xYsiSvhqblL8Vk/bRISGHZEK/75wFau2ZWDGwc3nTqKbz9awOCfN+Pz5XBdejobsgv5fOp2yuZ5iOvSiWYmh3//tB7f8ljimnsoSmjCKVOm48VPmMvBv4aeyy9vTOL6FZlgsxjQ/1S2Tl3GCXN24ff5OKtFMs094Tz67QbyfsrHNjX0D4vkjpk/41gchiMki4Gtm3Ptyo2sKynD4uDMow5+/v2/Go7ytrX2wj/dwQFsUx2MMe8B/7bW/mSM6Qdcb609f1/bajiKiMif81cOO3nmu9Ws3FFAm0aR3DioLcO6pgblkukiIvVVTQ1HOZB5plpW9aAH6RugO/ATgRlZJtXScUVEGgxrLd8s28Ez361ixfYCWiVF8O9zejC8W2OcCt8iIrXmr0J4hDGmv7V29r5WGmNOBg78uveH5m3gAWPMWUAz4L5aOq6ISL1nreW75Tt55rtVLNuaT8vECJ4+uzundG+i8C0iEgR/FcIfAb6uHPf9LrAe8AFtgLOAWyrva1zldIT/qHz4UW0cU0SkvrPWMnnFTp75bjVLtuTRPCGcJ8/szsgejXE5NZWZiEiw/GkIt9ZOM8ZcAbwK7H1pei9wi7X2y5oqTkREDo61likrM3nmu1UszsijWXwYj5/RjdN6NlH4FhGpA/7yI53W2k+MMd8BZwNdKp+zGvjYWru5husTEZEqsNYybXUWT3+7ikWbc2kSG8Y/T+/Kab2a4lb4FhGpMw5oXhVrbS7wUs2WIiIih2JjdhF//3QJs9dl0yQ2jEdO7coZvZvicSl8i4jUNQc/uaGIiNQJfr/ljVkbeOKblbgchgdGduacPmkK3yIidZhCuIhIPbY+q4jbPlnM3A05HNc+iUdO60pqTFiwyxIRkb+gEC4iUg/5/JbXZqznX5NWEuJy8OSZ3TmtVxNdXl5EpJ5QCBcRqWfW7Czg1k9+ZuGmXAZ3TObhU7uQHB0a7LJERKQKFMJFROoJr8/PK9PX8/R3qwj3OPn3OT04pXtj9X6LiNRDCuEiIvXAyu0F3PbJYhZn5HFS5xQeGNWZRlHq/RYRqa8UwkVE6rAKn58Xp6zl2cmriQp189x5PRnWNVW93yIi9ZxCuIhIHfXL1nxu/WQxy7bmM6xbKg+c0pmEyJBglyUiItVAIVxEpI4p9/r57w9r+O8Pa4gNd/PiBb04qUtqsMsSEZFqpBAuIlKHLN2Sxy0fL2bF9gJG9WjMvSM6ExfhCXZZIiJSzRTCRUTqAL/f8sLUtTz17SoSIjy8clE6J3RKDnZZIiJSQxTCRUSCLL+0gps/XMx3y3cwontjHhrZhZhwd7DLEhGRGqQQLiISRCu3F3D1O/PZvKuYe0d04pIjW2jmExGRw4BCuIhIkHyxeCu3ffIzkaEu3h/djz4t4oNdkoiI1BKFcBGRWlbh8/PY1yv434z19G4ex/Pn99Jl50VEDjMK4SIitSizoIzr3lvAT+t3cXH/5tw1rBMelyPYZYmISC1TCBcRqSULNuVwzTvzySup4Omzu3Nqz6bBLklERIJEIVxEpIZZa3nnp0088MUyUmJC+fSaI+ncOCbYZYmISBAphIuI1KDSCh93jV3KpwsyGNg+iWfO7kFsuC6+IyJyuFMIFxGpIZt3FXP1O/NZtjWfGwa15aZBbXE4NP2giIgohIuI1IhpqzK54YOF+PyWVy9KZ7CufikiIr+jEC4iUo38fsvzU9bw5LeraNcoipcu7E2LxIhglyUiInWMQriISDXJL63gbx8t5ttfdnBK98Y8dnpXwj36MSsiIn+k3w4iItVgzc5CRr81j427irlneCcuPUqXnxcRkf1TCBcROUSz12Zz1dvzcDsdvHdFX/q2Sgh2SSIiUscphIuIHIJP5mdwx2c/0zwhgtcv6UOz+PBglyQiIvWAQriIyEGw1vL0t6t4dvIajmqTwPPn9yYmzB3sskREpJ5QCBcRqaLSCh+3f/oz4xdt5ez0Zjx0ahfcTkewyxIRkXpEIVxEpAp2FZUz+q15zNuYw20nteeaY1vrA5giIlJlCuEiIgdoXWYhl74xl215pTx3Xk+Gd2sc7JJERKSeUggXETkAP63LZvTb83E6DO9f2Y/ezeOCXZKIiNRjCuEiIn/hswUZ3P7pz6TFh/P6JUeQlqAZUERE5NAohIuI7Ie1lme+W82/v19N/1YJvHhBb2LCNQOKiIgcOoVwEZF9KPP6+PunSxi7cAtn9G7KI6d2xePSDCgiIlI9FMJFRPaSU1TOVW/PZ86GXdw6pD1jBmoGFBERqV51IoQbY3pYaxcFuw4RkfVZRVz2xly25Jbw7Lk9OaW7ZkAREZHqF/QQbowZDrwCpFY+jgZuB+YBLa21T+2rLVj1ikjDNWf9Lka/PQ+HMbx/ZV96N48PdkkiItJABX2Ao7V2AvD7//PeBUy31o4FGhlj+u6nTUSk2oxftIULXv2J+AgPY8ccqQAuIiI1KughfB/6AYsqlxcDw/bTJiJyyKy1/Of71dz4wSJ6psXy2TVH0jwhIthliYhIAxf04Sj7kAIUVC4XAMn7aduDMWY0MBogLS2t5qsUkXqvwufnrrFL+GheBqf1bMKjp3clxOUMdlkiInIYqJUQbow5Cfj7PlZdZa1duVdbNhAJFFXeZ+2nbQ/W2peBlwHS09NttRUvIg1SQWkFY95dwPTVWdwwqC3/N7itZkAREZFaUysh3Fo7EZh4gJt/A3QHJgHdKh+X76NNROSgbM0t4bI35rJmZyGPn9GNs9KbBbskERE5zAR9OIox5mQgzhjTx1o7F3gCuNcYEwcUWGunGmPm7t0WzJpFpP5atjWPy96YS3GZjzcuPYKj2yYGuyQRETkMBT2EW2u/AkJ+97iYwHSE/FmbiEhV/bByJ9e9u4CYMDcfX9OfDinRwS5JREQOU0EP4SIiteG9nzZx9/ildEiJ4rVL+pAcHRrskkRE5DCmEC4iDZrfb3n8m5W8OHUtx7VP4rnzehERoh99IiISXPpNJCINVmmFj1s+XsyEn7dxft807j+lMy5nXbw8goiIHG4UwkWkQcopKmf02/OYuyGHvw/twFXHtNIUhCIiUmcohItIg7Mxu4hLXp/LltwSnjuvJ8O7NQ52SSIiIntQCBeRBmXBphyueHMe1lreu6Iv6S3ig12SiIjIHyiEi0iD8fWSbdz04SJSYkJ549IjaJkYEeySRERE9kkhXETqPWst/5uxnoe/Wk7PZrG8clE6CZEhf/1EERGRIFEIF5F6zevz88CEX3hr9kZO7prCU2f1INTtDHZZIiIif0ohXETqrdzicq59bwEz12Qz+phW/P2kDjgcmgFFRETqPoVwEamXVu8o4Iq35rEtt5QnzujGmenNgl2SiIjIAVMIF5F65/vlO7jxg0WEup28P7ofvZvHBbskERGRKlEIF5F6w1rLi1PX8fg3K+jSOIaXL+pNakxYsMsSERGpMoVwEakXSit83P7pz4xftJUR3Rvz+OndCPPoA5giIlI/KYSLSJ23Pa+U0W/PY8mWPG4d0p4xA1vrEvQiIlKvKYSLSJ22cFMOo9+eT3GZl5cvTOeETsnBLklEROSQKYSLSJ316fwM7hi7hJToUN69oi/tkqOCXZKIiEi1UAgXkTrH57f8c+IKXp62jv6tEnj+/F7ERXiCXZaIiEi1UQgXkTolr6SCG95fyNRVmVzcvzn/GN4Jt9MR7LJERESqlUK4iNQZ6zILueKteWzKLuaRU7tyXt+0YJckIiJSIxTCRaROmLoqk+veW4Db6eDdK/rSt1VCsEsSERGpMQrhIhJU1lr+N2M9j3y1nHbJUbxyUTrN4sODXZaIiEiNUggXkaApLPPyj7FLGLdoKyd1TuHJs7oTEaIfSyIi0vDpt52IBMWSjDyuf38Bm3YVc/MJ7bjuuDY4HLoAj4iIHB4UwkWkVvn9geEnj3+zgsTIED4Y3Z8jWsYHuywREZFapRAuIrUmq7CMv320mKmrMjmxUzKPn9GN2HDN/y0iIocfhXARqRXTV2fyfx8uJr+0ggdHduaCfs0xRsNPRETk8KQQLiI1qsLn51+TVvLS1HW0aRTJO1ccQYeU6GCXJSIiElQK4SJSYzZlF3P9BwtZvDmXc49oxj3DOxPmcQa7LBERkaBTCBeRGvH54q3c9dkSMPDf83oxrFtqsEsSERGpMxTCRaRaFZd7uXf8Mj6en0GvtFj+fU5PXXxHRERkLwrhIlJtlm3N4/r3F7I+q4jrjmvDTYPb4nI6gl2WiIhInaMQLiKHzFrLG7M28OhXK4gNd/Pu5X05sk1isMsSERGpsxTCReSQ7Coq57ZPFvPd8p0M6tCIJ87sTnyE5v4WERH5MwrhInLQfli5k79/+jM5RRXcM7wTlx7VQnN/i4iIHACFcBGpsm15JTzwxS98vXQ7rZMi+N/FfejSJCbYZYmIiNQbCuEicsC8Pj9vzNrA09+uwuu33HJiO648phUhLs39LSIiUhUK4SJyQOZvzOEf45ayfFs+A9sn8cApXUhL0NSDIiIiByOoIdwY0wz4D9ALeNtae5cxJhq4HZgHtLTWPrWvtqAVLXKYyS0u558TV/L+nE2kRIfywvm9OKlLisZ+i4iIHIJg94QfA5wORAGrjDHPAjcDP1hrJxpjHjPG9AVO27vNWvtTEOsWafCstXy6YAuPfrWc3JIKrji6JTed0I7IkGD/2BAREan/gv3b9GNrrQ/INcYsB4qAfsDTlesXA8P206YQLlJDVu8o4B/jlvLT+l30TIvl7VFd6dQ4OthliYiINBhBDeHW2nIAY0wjYLK1ttAYkwIUVG5SACQD+2rbgzFmNDAaIC0trYYrF2mYSsp9PDt5Na9MW0dEiItHT+vK2enNcDg09ERERKQ61UoIN8acBPx9H6uuAlYDo4CHKtuygUgCveKRQNZ+2vZgrX0ZeBkgPT3dVusLEDkMfL98B/d+voyMnBJO79WUO0/uQEJkSLDLEhERaZBqJYRbaycCE/e1zhhzFoEPZfqMMc2Bb4DuwCSgW+Xj8n20iUg12JJbwv2fL2PSLzto2yiSD0f3o2+rhGCXJSIi0qAFe3aU24GrgXuMMR7gb8ATwL3GmDigwFo71Rgzd++24FUt0jCUeX28OWsDz3y3Gr+13HZSe644uhUelyPYpYmIiDR4xtqGN3IjPT3dzps3L9hliNRJpRU+Ppy7mRemrGV7fimDOjTivlM60yxec36LiIhUlTFmvrU2varPC/bsKCJSS0orfLw/ZxMvTl3Ljvwy+rSI419ndueoNgma81tERKSWKYSLNHClFT7e/WkTL01dy86CMvq2jOfps3vQv5XCt4iISLAohIs0UCXlPt79aSMvTl1HVmEZ/VrF8+9zetK/tT50KSIiEmwK4SINTHG5l3d+3MjL09aRVVjOka0T+O95PTXjiYiISB2iEC7SQBSVeXn7x428Mm0d2UXlHN0mkRsHt6VPi/hglyYiIiJ7UQgXqeeKyry8NXsjr0xfx66icga0TeSmwW3p3VzhW0REpK5SCBeppwrLvLw5awOvTl9HTnEFx7ZL4oZBbendPC7YpYmIiMhfUAgXqWeWbsnjg7mbGL9oKwWlXo5rHwjfPdMUvkVEROoLhXCReiC3uJxxC7fw0bwMftmWT4jLwdAuKVx6VEu6N4sNdnkiIiJSRQrhInWU32+ZvS6bD+duZuKy7ZR7/XRpEs2DIztzSo8mxIS5g12iiIiIHCSFcJE6ZmtuCZ/Mz+Dj+ZvZvKuE6FAX5/Zpxll9mtG5cUywyxMREZFqoBAuUgeUe/18t3wHH87dzLTVmVgLR7ZO4JYT2zOkcwqhbmewSxQREZFqpBAuEkSrdhTw4dzNjF24hV1F5aREh3LdcW04s3cz0hLCg12eiIiI1BCFcJFalpFTzA8rdvLZwi0s3JSL22kY3DGZs/o045i2STgdJtglioiISA1TCBepYV6fnwWbcpm8Yic/rNjJyh0FALRpFMldJ3fk1F5NSIwMCXKVIiIiUpsUwkVqQE5ROVNXZTJ5xU6mrsokr6QCl8PQp0U8d53ckeM6NKJ1UgTGqNdbRETkcKQQLlINrLWs2F7A5BU7mbxiJws35eC3kBjp4YROyRzfoRFHt00kOlTTCoqIiIhCuMhBKyn3MXNNFpNXBoaZbMsrBaBrkxiuO74tx3doRLcmMTg0xltERET2ohAucoBKyn0s3ZrHwk05zFqbzey12ZR5/UR4nBzdNpGbBrfluPaNaBQdGuxSRUREpI5TCBfZB2stG7OLWbg5h4Wbclm4KZfl2/Lx+i0ALRMjOK9vGoM6JNOnZRwhLs3jLSIiIgdOIVwEyC+t4OfNgV7uhZtzWbgph5ziCgAiPE66N4vlqmNb0bNZHD3SYjWbiYiIiBwShXA57Pj8ljU7CwOBe1MuCzfnsHpnITbQyU3bRpGc0CmZnmlx9EyLpW2jKM3dLSIiItVKIVwaLJ/fkpFTzJqdhb/dMgtZvaOQwjIvALHhbno2i2V4t8b0TIulW9NYYsI0g4mIiIjULIVwqffKvD7WZxXtGbZ3FrIuq4hyr3/3dklRIbRJiuS0Xk3o0SyWnmlxtEgI11zdIiIiUusUwqVe8PstWYVlZOSWsLayR3ttZdjetKuYys9LYgw0jQujTVIkx7RLok1SJK0bRdImKZKYcPVwi4iISN2gEC51QmGZl625JWzJLWFbbilbc0t+e5xXyra8Eip8dvf2bqehZWIEnRpHc0r3xoGg3SiS1kmRhLo1U4mIiIjUbQrhUqP8fktuSQXZhWVkF5WzI7+ULZUBe+vvwnZ+qXeP5zkdhpToUBrHhtKjWSwnd02lSWwoqTFhtEqKIC0+HJfTEaRXJSIiInJoFMKlSvx+S15JBdlFZWQXlrOrqJysonJ2FZYH2n63vKsosN5v/7if2HA3jWPCaBoXzhEt42kcG0bj2LDdQbtRVIhCtoiIiDRYCuGHGb/fUlDmpaC0goJSLwWlXvJLKigo2/Nxfmlgm1/vC0q95BZXkFNcjm9fqRqICXOTEOEhIdJDy8QIejePJzHSQ3yEh4TIEBIiPCRX9m6He/StJyIiIocvJaE6xlpLmddPmddPuddPmddHmddPSbmP4nIfxeXe35YrfJSUeyku91FS4dvdXlK53a/txeU+issCAbuw3Lt7Puz98bgcRIe6iA51ExXqIirUTWpMaGXIDqkM1Z7dy4mRHuIiPLjVcy0iIiJyQBpkCLcEpq3z+8Hr9+PzW7x+i7/y3rf73o/Xb/H6Am0+W7nO9+s2fry+wH25z+L1+anw+anYvWyp8Pup8P66TeX2vj23L/f5Kav47f7XYF1eGbbLvL5Au8+/x5R6VeFxOQj3OAl3Own1OCuXXcSFe2gS6yTc4yIq1BUI12G/hevfgvZv7boEu4iIiEjNapAhfOmWPNr/Y2KtHtNhwO104HY6cDlNYNlhcDkdhLgchLgdhLiceJwO4iI8hLgceFzOwDqXA48rsP63ZQchbichzsBzw9yBIB3mcRDmdgVCtsdJmMdJmNup8dMiIiIi9UiDDOHJ0aHcOqQ9DmNwOw0OY3A5DU6HweUwOB2OyvvAY8fudoPL4QjcVz7P/Wugrrx3/brscOB2Bfbjdjp0WXMREREROWANMoQ3igrh2uPaBLsMEREREZF90hgGEREREZFaphAuIiIiIlLLFMJFRERERGqZQriIiIiISC1TCBcRERERqWVBDeHGmBbGmP8aY6YbY86vbIs2xjxsjDnVGHPz/tpEREREROqrYPeEJ1prrwVGAKdXtt0FTLfWjgUaGWP67qdNRERERKReCmoIt9bOq1wcAjxVudwPWFS5vBgYtp82EREREZF6KegX6zHGdAfOBuKA44AUoKBydQGQvJ+2vfczGhhd+bDUGLNsH4dLBLKqrfiDEwPkBXlfVXnegWz7Z9tUdd2+2urCeYOGd+4Odn1V2uvCuavO83Yo+zvQ5x3qe+7P1uvcVX1/9e3nJTS8c1cXfl7+1Tb6XVe9+6pvv+va/1mB+2WtrfEbcBIwZR+39r/bZjKQBMwCkivbzgEe3lfbXxzv5f20z6uN13swtdXmvqryvAPZ9s+2qeq6/bQF/bw1xHN3sOur0l4Xzl11nrfaOHeH+p7Tuave/dW3n5cN8dzVhZ+XtXHu6sJ5a4jnri7/vKyVnnBr7URg4l9stgHYBXwDdAcmAd0qH5fvo+3PfHEI5da06qztYPdVlecdyLZ/tk1V1+ncVd/z/mrbg11f1fZgq+66avrcHep77s/W69zV7HP08/I3De3n5V9to3NXvfs6LH7XmcoEHxTGmNcIBO/pwHZr7U/GmHDgXmAB0Mpa++i+2g7yePOstenVVL7UEp23+kvnrv7Suau/dO7qJ523+utgz11Qx4Rbay/bR1sxcPtftR2kl6thH1L7dN7qL527+kvnrv7SuaufdN7qr4M6d0HtCRcRERERORwFe55wEREREZHDjkK4iIiIiEgtUwgXEREREallDS6EG2O6GmOcVdi+szFmtDHmjpqsS/7cQZy3Y40xDxpj3q3JuqRmGGNONMbEBrsOCfir958xZqQxZoAx5sbarEuqh95vddMBvO/0e64OOoDzdsC5skGFcGNMP+BHwG2McVV+855qjLnTGLPP12qtXQYsp3qvyiZVcDDnDZhrrb0b2FR7lcq+HMQfUG4CV8eNrbGi5IAd4PtvqLV2OuAzxnQNXrWytwMIBHq/1UEH+L7T77k65kDOW1VyZYMK4dbaH4HMyodXAlustWMJzEV+JoAx5mxjzDu/uw0A5gBHBKVoOajzBvQ2xrRFUzoF1UH+4VsBlNVqobJfB/L+A8yvm1fepA44wECg91sddCDvO2ttsX7P1S0H+PMSDjBXNqgQvpd+wKLK5cXAMABr7YfW2gt+vQHxBL5QnwSlStnbgZ63ZOBG4HJjTPOgVCqH8oev1E37fP8B3xlj+gMea+3SYBQmf1SFQCB12z7fd8aYM9Dvubpsf+dtJAeYK4N6sZ4algIUVC4XEAhtf2CtHV9rFcmBONDz9gn6w6mu6Qe8ULm8GLgG+NBa+yHw4a8bVf57/DKgBbChdkuUv7DP95+19uPKttnBKEoOyD7ff5Xvt+bo/VaX7e99p99zddv+ztsB58qGHMKzgcjK5UggK4i1yIHTeau/DvQPqArg0toqSqpE77/6a3+BQO+3uk/vu/rpkM9bQx6O8g3QvXK5GzApiLXIgdN5q7/0i6T+0/uv/tL7r/7S+65+OuTz1qBCuDEmHUgCTgTeBtKMMWcBzYB3glmb7J/OW4OhXyT1kN5/DYbef/WI3nf1U3WfN2OtPuguIgen8gfSVOBcYALwAPAz0BW4z1rrC2J5Ig2a3n8i9ZtCuIiIiIhILWtQw1FEREREROoDhXARERERkVqmEC4iIiIiUssUwkVEREREaplCuIiIiIhILVMIFxE5jBhjoo0x3Q5gO2OM6VsbNYmIHI4UwkVEGiBjzOXGmOF7tfUCZgE3H8AuHgJm1kRtIiKiEC4i0lCNBq75fYO1dgHw6QE+/5Vqr0hERHZTCBcRaWAqh5tsBU4yxrTca7Wu0CYiUgcohIuINDwXAVcAa4Gr9reRMeYyY8w8Y8wlxpjFxpgdxpgr99rmVGPMbGPMRmNM78q2KGPMc8aYK4wx7xpjTqzRVyMi0gAphIuINCDGmDAgzFqbTWBIyWXGmJD9bD4W6A0Ya2134DHgBWNMm8r1DsBnre0PfALcUNl+HtDaWvsq8Hbl80REpAoUwkVEGpazgY8rl98AYoAz9rWhtTancvGHyvv/AHnACZWPDTCpcnkZ0LRy+UPgBmNMBNAHiKum2kVEDhuuYBcgIiLV6mxgpzHmksrHO4AxwLt/9URrrdcYsx4I28dqP+Cs3C7XGHMpUAbMBy6vhrpFRA4rCuEiIg2EMaYLMNVa+9jv2k4Bxhtjullrfz6A3UQCy//iODcAvay1FxpjBh5CySIihy0NRxERaThu4I893hOA7cB1lY/39XM/AsAY0w7wEhiC4threweB4SkQGK7y63I6EGWM8Rxq8SIihxOFcBGRBsAYcxFwMTB4r1VHEhhKcokx5lpgKJBujOn5u22uNsbcB9wLnG6t9QEXVK67zBjTCBgBdKh83nvAKcaY6QR6zX3ow5kiIlVirNWUsSIihytjjAVaWms3BLsWEZHDiXrCRUTE/PUmIiJSnRTCRUQOU8aYqysXLzHGpAS1GBGRw4yGo4iIiIiI1DL1hIuIiIiI1DKFcBERERGRWqYQLiIiIiJSyxTCRURERERqmUK4iIiIiEgtUwgXEREREall/w8cLt2xd27GkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "岭回归随着lambda->Inf，压缩惩罚项影响力增加，岭回归系数估计值越接近0.\n",
    "见下图。\n",
    "'''\n",
    "##超参数lambda，有的教材也用alpha\n",
    "\n",
    "# 设置matplotlib参数，正确显示中文和'-'符号。\n",
    "plt.rcParams.update(\n",
    "    {\n",
    "        'text.usetex': False,\n",
    "        'font.family': 'stixgeneral',\n",
    "        'mathtext.fontset': 'stix',\n",
    "    }\n",
    ")\n",
    "alpha=10**np.linspace(-5,10,100)\n",
    "intercept_dict = {}\n",
    "for a in alpha:\n",
    "    result = model.fit_regularized(L1_wt=0,alpha=a,refit=True,profile_scale=True)\n",
    "    ###保存每次迭代的回归系数估计值\n",
    "    intercept_dict[a]=result.params\n",
    "###lambda参数与对应的系数估计值，去掉截距项\n",
    "params_l2 = pd.DataFrame(intercept_dict).T\n",
    "params_l2=params_l2[[1,2,3,4]]\n",
    "###行索引是alpha值，列索引是自变量名\n",
    "params_l2.columns=['Income','Limit','Rating','Student']\n",
    "plt.figure(figsize = (12,6))\n",
    "\n",
    "###Seaborn使用行索引为X轴，每一列的数据为Y轴，绘制曲线\n",
    "#列名作为示意图的标记。\n",
    "sns.lineplot(data = params_l2,dashes=True)\n",
    "plt.axhline(y = 0,linestyle = 'dashed',lw = 0.8,color = 'black')\n",
    "plt.xticks(alpha)\n",
    "###对X轴进行对数转换伸缩。\n",
    "plt.xscale('log')\n",
    "plt.ylim(-300,400)\n",
    "plt.xlim(10**(-3)-0.0001,10**3)\n",
    "plt.ylabel('Coeffients',size=14)\n",
    "plt.xlabel('Alpha',size=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Student</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.000000e-05</th>\n",
       "      <td>-2.796386e+02</td>\n",
       "      <td>2.805992e+02</td>\n",
       "      <td>3.383925e+02</td>\n",
       "      <td>1.267993e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.417474e-05</th>\n",
       "      <td>-2.796328e+02</td>\n",
       "      <td>2.806337e+02</td>\n",
       "      <td>3.383521e+02</td>\n",
       "      <td>1.267988e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.009233e-05</th>\n",
       "      <td>-2.796245e+02</td>\n",
       "      <td>2.806825e+02</td>\n",
       "      <td>3.382949e+02</td>\n",
       "      <td>1.267980e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.848036e-05</th>\n",
       "      <td>-2.796127e+02</td>\n",
       "      <td>2.807513e+02</td>\n",
       "      <td>3.382142e+02</td>\n",
       "      <td>1.267970e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.037017e-05</th>\n",
       "      <td>-2.795961e+02</td>\n",
       "      <td>2.808481e+02</td>\n",
       "      <td>3.381004e+02</td>\n",
       "      <td>1.267955e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.477076e+09</th>\n",
       "      <td>8.594953e-08</td>\n",
       "      <td>1.597357e-07</td>\n",
       "      <td>1.600930e-07</td>\n",
       "      <td>4.801494e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.511192e+09</th>\n",
       "      <td>6.063569e-08</td>\n",
       "      <td>1.126904e-07</td>\n",
       "      <td>1.129425e-07</td>\n",
       "      <td>3.387359e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.977024e+09</th>\n",
       "      <td>4.277728e-08</td>\n",
       "      <td>7.950082e-08</td>\n",
       "      <td>7.967869e-08</td>\n",
       "      <td>2.389715e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.054802e+09</th>\n",
       "      <td>3.017853e-08</td>\n",
       "      <td>5.608626e-08</td>\n",
       "      <td>5.621174e-08</td>\n",
       "      <td>1.685897e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000000e+10</th>\n",
       "      <td>2.129035e-08</td>\n",
       "      <td>3.956774e-08</td>\n",
       "      <td>3.965627e-08</td>\n",
       "      <td>1.189367e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Income         Limit        Rating       Student\n",
       "1.000000e-05 -2.796386e+02  2.805992e+02  3.383925e+02  1.267993e+02\n",
       "1.417474e-05 -2.796328e+02  2.806337e+02  3.383521e+02  1.267988e+02\n",
       "2.009233e-05 -2.796245e+02  2.806825e+02  3.382949e+02  1.267980e+02\n",
       "2.848036e-05 -2.796127e+02  2.807513e+02  3.382142e+02  1.267970e+02\n",
       "4.037017e-05 -2.795961e+02  2.808481e+02  3.381004e+02  1.267955e+02\n",
       "...                    ...           ...           ...           ...\n",
       "2.477076e+09  8.594953e-08  1.597357e-07  1.600930e-07  4.801494e-08\n",
       "3.511192e+09  6.063569e-08  1.126904e-07  1.129425e-07  3.387359e-08\n",
       "4.977024e+09  4.277728e-08  7.950082e-08  7.967869e-08  2.389715e-08\n",
       "7.054802e+09  3.017853e-08  5.608626e-08  5.621174e-08  1.685897e-08\n",
       "1.000000e+10  2.129035e-08  3.956774e-08  3.965627e-08  1.189367e-08\n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###不同lambda值下的回归系数，\n",
    "params_l2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### 示例2：Lasso\n",
    "\n",
    "相对于岭回归不做自变量的选择，lasso可以选择自变量，将不重要自变量的系数压缩至0。\n",
    "\n",
    "因此称lasso模型为稀疏模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Lasso变量选择，某些自变量系数估计值压缩为零。\n",
    "'''\n",
    "X = data\n",
    "scaler = StandardScaler()\n",
    "X[['Income','Limit','Rating','Cards','Age','Education']]=scaler.fit_transform(\n",
    "    X[['Income','Limit','Rating','Cards','Age','Education']])\n",
    "X['Balance']=data['Balance']\n",
    "\n",
    "###模型包含了所有自变量，对于因子变量通过patsy的C函数转换成category类别变量\n",
    "formula = 'Balance~Income+Limit+Rating+Cards+Age+Education+C(Gender)\\\n",
    "               +C(Student)+C(Married)+C(Ethnicity)'\n",
    "model=smf.ols(formula,data=X)\n",
    "result_ols=model.fit()\n",
    "alpha=10**np.linspace(-2,4,100)\n",
    "###系数的绝对值之和\n",
    "betaols_l1=np.sum(np.abs(result_ols.params))\n",
    "params_l1 = {}\n",
    "\n",
    "params2_l1 = {}\n",
    "for a in alpha:\n",
    "    result = model.fit_regularized(L1_wt=1,alpha=a)\n",
    "    params=result.params\n",
    "    params_l1[a]=params\n",
    "    rate=np.sum(np.abs(params))/betaols_l1\n",
    "    ###保存每个alpha参数回归之后的系数与未正则化模型的系数\n",
    "    #的l1范数之比作为字典的键，以系数作作为值\n",
    "    #用于下个代码单元绘制正则化路径图。\n",
    "    params2_l1[rate] = params\n",
    "    \n",
    "###lambda参数与对应的系数估计值，去掉截距项\n",
    "params_l1 = pd.DataFrame(params_l1).T\n",
    "params2_l1 = pd.DataFrame(params2_l1).T\n",
    "del params_l1['Intercept']\n",
    "del params2_l1['Intercept']\n",
    "plt.figure(figsize = (10,6))\n",
    "sns.lineplot(data = params_l1,dashes=False)\n",
    "plt.axhline(y = 0,lw = 0.1,color = 'black')\n",
    "plt.xticks(alpha,fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.xscale('log')\n",
    "plt.ylabel('Coeffients',size=14)\n",
    "plt.xlabel('Alpha',size=14)\n",
    "plt.legend(bbox_to_anchor=(1, 0), loc=3, borderaxespad=2,fontsize=14)\n",
    "\n",
    "###在曲线上标注系数名(只标注值大于10的系数)\n",
    "pnames=params_l1.iloc[50].iloc[np.where(np.abs(params_l1.iloc[50])>10)]\n",
    "xn=pnames.name\n",
    "for p in pnames.index:\n",
    "    yn=pnames.loc[p]\n",
    "    plt.text(xn-2,yn,p,fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['C(Student)[T.Yes]', 'Income', 'Limit', 'Rating', 'Cards'], dtype='object'),\n",
       " 10.722672220103231,\n",
       " C(Student)[T.Yes]    315.840489\n",
       " Income              -224.199504\n",
       " Limit                513.030478\n",
       " Rating                51.418883\n",
       " Cards                 19.552916\n",
       " Name: 10.722672220103231, dtype: float64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pnames.index,pnames.name,pnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>C(Student)[T.Yes]</th>\n",
       "      <th>C(Married)[T.Yes]</th>\n",
       "      <th>C(Ethnicity)[T.Asian]</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5722.367659</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6579.332247</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7564.633276</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8697.490026</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000.000000</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              C(Gender)[T.Male]  C(Student)[T.Yes]  C(Married)[T.Yes]  \\\n",
       "0.010000              10.563768         428.526534          -5.653529   \n",
       "0.011498              10.605433         428.690293          -6.280641   \n",
       "0.013219              10.600946         428.673285          -6.268550   \n",
       "0.015199              10.595786         428.653730          -6.254647   \n",
       "0.017475              10.589855         428.631247          -6.238663   \n",
       "...                         ...                ...                ...   \n",
       "5722.367659            0.000000           0.000000           0.000000   \n",
       "6579.332247            0.000000           0.000000           0.000000   \n",
       "7564.633276            0.000000           0.000000           0.000000   \n",
       "8697.490026            0.000000           0.000000           0.000000   \n",
       "10000.000000           0.000000           0.000000           0.000000   \n",
       "\n",
       "              C(Ethnicity)[T.Asian]  C(Ethnicity)[T.Caucasian]      Income  \\\n",
       "0.010000                   8.435623                   0.000000 -275.084080   \n",
       "0.011498                  15.093877                   9.870837 -274.944679   \n",
       "0.013219                  15.078504                   9.859365 -274.936638   \n",
       "0.015199                  15.060829                   9.846176 -274.927392   \n",
       "0.017475                  15.040507                   9.831011 -274.916762   \n",
       "...                             ...                        ...         ...   \n",
       "5722.367659                0.000000                   0.000000    0.000000   \n",
       "6579.332247                0.000000                   0.000000    0.000000   \n",
       "7564.633276                0.000000                   0.000000    0.000000   \n",
       "8697.490026                0.000000                   0.000000    0.000000   \n",
       "10000.000000               0.000000                   0.000000    0.000000   \n",
       "\n",
       "                   Limit     Rating      Cards        Age  Education  \n",
       "0.010000      583.675173  32.274219  30.404194 -10.544515  -4.268603  \n",
       "0.011498      583.625610  32.322465  30.412895 -10.411943  -4.151370  \n",
       "0.013219      583.615496  32.323988  30.411364 -10.410843  -4.149627  \n",
       "0.015199      583.603867  32.325740  30.409604 -10.409578  -4.147623  \n",
       "0.017475      583.590496  32.327755  30.407580 -10.408124  -4.145319  \n",
       "...                  ...        ...        ...        ...        ...  \n",
       "5722.367659     0.000000   0.000000   0.000000   0.000000   0.000000  \n",
       "6579.332247     0.000000   0.000000   0.000000   0.000000   0.000000  \n",
       "7564.633276     0.000000   0.000000   0.000000   0.000000   0.000000  \n",
       "8697.490026     0.000000   0.000000   0.000000   0.000000   0.000000  \n",
       "10000.000000    0.000000   0.000000   0.000000   0.000000   0.000000  \n",
       "\n",
       "[100 rows x 11 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_l1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "下图可以看出变量进入模型的先后顺序-即所谓“正则化路径（regularization path）”\n",
    "先后进入模型的变量依次是：Rating,Limit、Student和Income\n",
    "'''\n",
    "plt.figure(figsize = (10,6))\n",
    "sns.lineplot(data = params2_l1,dashes=False)\n",
    "plt.axhline(y = 0,lw = 0.1,color = 'black')\n",
    "plt.xticks(alpha)\n",
    "plt.xscale('log')\n",
    "plt.ylabel('Coeffients',fontsize=14)\n",
    "plt.xlabel('|beta_i|/|beta_ls|',fontsize=14)\n",
    "plt.legend(bbox_to_anchor=(1, 0), loc=3, borderaxespad=2,fontsize=14)\n",
    "\n",
    "###在曲线上标注系数名(注：此处只标注值绝对值大于5的系数)，在第59个数据处进行标注\n",
    "pnames=params2_l1.iloc[59].iloc[np.where(np.abs(params2_l1.iloc[59])>5)]\n",
    "xn=pnames.name##lambda的值\n",
    "for p in pnames.index:\n",
    "    yn=pnames.loc[p]\n",
    "    plt.text(xn-0.1,yn,p,fontsize=14)\n",
    "plt.xticks(size=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### 正则化与变量选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.51901361,  0.24543935,  0.08758112,  0.31653363,  0.13040066,\n",
       "        0.25740861,  0.21206243,  0.39471856,  0.39560115,  0.03963808,\n",
       "        0.00073846, -0.00367584])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 岭回归的模型正则化\n",
    "#alpha=1000时，系数被缩减到0附近\n",
    "result_test1 = model.fit_regularized(L1_wt=0,alpha=1000)\n",
    "result_l2=result_test1.params\n",
    "result_l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept                    478.148450\n",
       "C(Gender)[T.Male]              0.000000\n",
       "C(Student)[T.Yes]            218.643566\n",
       "C(Married)[T.Yes]              0.000000\n",
       "C(Ethnicity)[T.Asian]          0.000000\n",
       "C(Ethnicity)[T.Caucasian]      0.000000\n",
       "Income                      -180.170159\n",
       "Limit                        451.360756\n",
       "Rating                        68.799025\n",
       "Cards                          9.842547\n",
       "Age                            0.000000\n",
       "Education                      0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Lasso的变量选择\n",
    "#设置alpha=4，很多系数被缩减为0\n",
    "result_test = model.fit_regularized(L1_wt=1,alpha=20)\n",
    "result_l1=result_test.params\n",
    "result_l1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### 最优$\\lambda$参数选择\n",
    "选择fit_regularized函数参数$\\alpha$的最优值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(     Student_No  Student_Yes\n",
       " 1             1            0\n",
       " 2             0            1\n",
       " 3             1            0\n",
       " 4             1            0\n",
       " 5             1            0\n",
       " ..          ...          ...\n",
       " 396           1            0\n",
       " 397           1            0\n",
       " 398           1            0\n",
       " 399           1            0\n",
       " 400           1            0\n",
       " \n",
       " [400 rows x 2 columns],\n",
       "      Income     Limit    Rating     Cards       Age  Education   Balance  \\\n",
       " 1 -0.861583 -0.489999 -0.465539 -0.699130 -1.257674  -0.784930 -0.407277   \n",
       " 2  1.727437  0.828261  0.828703  0.031032  1.528451   0.496588  0.834056   \n",
       " 3  1.686756  1.014787  1.029311  0.761194  0.889964  -0.784930  0.130634   \n",
       " 4  2.946152  2.068440  2.110003  0.031032 -1.141586  -0.784930  0.966900   \n",
       " 5  0.302928  0.070012  0.013331 -0.699130  0.715831   0.816968 -0.411633   \n",
       " \n",
       "    Student_Yes  Gender_Male  Married_Yes  Ethnicity_Asian  Ethnicity_Caucasian  \n",
       " 1            0            1            1                0                    1  \n",
       " 2            1            0            1                1                    0  \n",
       " 3            0            1            0                1                    0  \n",
       " 4            0            0            0                1                    0  \n",
       " 5            0            1            1                0                    1  )"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1=data.copy()\n",
    "dummies1 = pd.get_dummies(X1.Student, prefix='Student')\n",
    "dummies2 = pd.get_dummies(X1.Gender, prefix='Gender')\n",
    "dummies3 = pd.get_dummies(X1.Married, prefix='Married')\n",
    "dummies4 = pd.get_dummies(X1.Ethnicity, prefix='Ethnicity')\n",
    "X1=X1.drop('Student',axis=1).join(dummies1)\n",
    "X1=X1.drop('Gender',axis=1).join(dummies2)\n",
    "X1=X1.drop('Married',axis=1).join(dummies3)\n",
    "X1=X1.drop('Ethnicity',axis=1).join(dummies4)\n",
    "X1=X1.drop('Student_No',axis=1)\n",
    "X1=X1.drop('Gender_Female',axis=1)\n",
    "X1=X1.drop('Married_No',axis=1)\n",
    "X1=X1.drop('Ethnicity_African American',axis=1)\n",
    "scaler = StandardScaler()\n",
    "X1[['Income','Limit','Rating','Cards','Age','Education','Balance']]=scaler.fit_transform( \\\n",
    "    X1[['Income','Limit','Rating','Cards','Age','Education','Balance']])\n",
    "#X1['Balance']=data['Balance']\n",
    "dummies1,X1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept             -0.108993\n",
      "Income                -0.585111\n",
      "Limit                  1.088484\n",
      "Rating                 0.238970\n",
      "Cards                  0.058086\n",
      "Age                   -0.022707\n",
      "Education             -0.002364\n",
      "Student_Yes            0.926261\n",
      "Gender_Male            0.030776\n",
      "Married_Yes           -0.036078\n",
      "Ethnicity_Asian        0.026058\n",
      "Ethnicity_Caucasian    0.032685\n",
      "dtype: float64\n",
      "MSE: 0.040116497819261986 \n",
      "aplha: 0.0003409285069746815\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "岭回归最优lambda参数选择\n",
    "'''\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import Ridge,RidgeCV,LassoLarsIC\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#拆分为训练集和测试集\n",
    "predictors=['Income','Limit','Rating','Cards','Age','Education',\n",
    "            'Student_Yes','Gender_Male','Married_Yes',\n",
    "            'Ethnicity_Asian','Ethnicity_Caucasian']\n",
    "            \n",
    "x_train,x_test,y_train,y_test=model_selection.train_test_split(X1[predictors],\n",
    "                                                               X1.Balance,test_size=0.2,\n",
    "                                                               random_state=1234)\n",
    "\n",
    "#构造不同的lambda值\n",
    "Lambdas=np.logspace(-10,10,200)\n",
    "#设置交叉验证的参数，使用均方误差评估\n",
    "ridge_cv=RidgeCV(alphas=Lambdas,normalize=True,scoring='neg_mean_squared_error',cv=10)\n",
    "ridge_cv.fit(x_train,y_train)\n",
    "\n",
    "#基于最佳lambda值建模\n",
    "ridge=Ridge(alpha=ridge_cv.alpha_,normalize=True)\n",
    "ridge.fit(x_train,y_train)\n",
    "#打印回归系数\n",
    "print(pd.Series(index=['Intercept']+x_train.columns.tolist(),\n",
    "                data=[ridge.intercept_]+ridge.coef_.tolist()))\n",
    "\n",
    "#模型评估\n",
    "ridge_pred=ridge.predict(x_test)\n",
    "#均方误差\n",
    "MSE=mean_squared_error(y_test,ridge_pred)\n",
    "print('MSE:',MSE,'\\naplha:',ridge_cv.alpha_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系数列表：                              \n",
      "Intercept           -0.072096\n",
      "Income              -0.272319\n",
      "Limit                0.601981\n",
      "Rating               0.412965\n",
      "Cards                0.000000\n",
      "Age                 -0.000000\n",
      "Education            0.000000\n",
      "Student_Yes          0.704441\n",
      "Gender_Male          0.000000\n",
      "Married_Yes         -0.000000\n",
      "Ethnicity_Asian      0.000000\n",
      "Ethnicity_Caucasian  0.000000\n",
      "\n",
      "MSE: 0.1009724472137844 \n",
      "\n",
      "最优lambda： 0.0036543830709572546\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Lasso的Lambda最优值选择。\n",
    "使用sklearn相关函数，比如LassoCV等。\n",
    "'''\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import Lasso,LassoCV\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "predictors=['Income','Limit','Rating','Cards','Age','Education',\n",
    "            'Student_Yes','Gender_Male','Married_Yes',\n",
    "            'Ethnicity_Asian','Ethnicity_Caucasian']\n",
    "x_train,x_test,y_train,y_test=model_selection.train_test_split(X1[predictors],\n",
    "                                                               X1.Balance,test_size=0.2,\n",
    "                                                               random_state=1234)\n",
    "#构造不同的lambda值\n",
    "Lambdas=np.logspace(-5,10,200)\n",
    "#设置交叉验证的参数，使用均方误差评估\n",
    "lasso_cv=LassoCV(alphas=Lambdas,normalize=False,cv=10,max_iter=10000)\n",
    "lasso_cv.fit(x_train,y_train)\n",
    "\n",
    "#基于最佳lambda值建模\n",
    "lasso=Lasso(alpha=lasso_cv.alpha_,normalize=True,max_iter=10000)\n",
    "lasso.fit(x_train,y_train)\n",
    "#打印回归系数\n",
    "print('系数列表：',pd.DataFrame(index=['Intercept']+x_train.columns.tolist(),columns=[''],\n",
    "                data=[lasso.intercept_]+lasso.coef_.tolist()))\n",
    "\n",
    "#模型评估\n",
    "lasso_pred=lasso.predict(x_test)\n",
    "#均方误差\n",
    "MSE=mean_squared_error(y_test,lasso_pred)\n",
    "print('\\nMSE:',MSE,'\\n\\n最优lambda：',lasso_cv.alpha_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系数列表:                              \n",
      "Intercept           -0.085427\n",
      "Income              -0.568149\n",
      "Limit                0.881575\n",
      "Rating               0.428355\n",
      "Cards                0.045606\n",
      "Age                 -0.019789\n",
      "Education            0.000000\n",
      "Student_Yes          0.854274\n",
      "Gender_Male          0.000000\n",
      "Married_Yes          0.000000\n",
      "Ethnicity_Asian      0.000000\n",
      "Ethnicity_Caucasian  0.000000\n",
      "\n",
      "Alpha值: 0.0061990099614554995\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "使用LassoLarsIC函数通过AIC选择alpha值。\n",
    "'''\n",
    "X2=X1[predictors]\n",
    "Y2=X1.Balance\n",
    "model_aic = LassoLarsIC(criterion='aic',normalize=False)\n",
    "model_aic.fit(X2,Y2)\n",
    "alpha_aic_ = model_aic.alpha_\n",
    "coefs_values=np.append([model_aic.intercept_],model_aic.coef_)\n",
    "coefs_names=np.append(['Intercept'],predictors)\n",
    "coefs=pd.DataFrame(coefs_values,index=coefs_names,columns=[' '])\n",
    "print('系数列表:',coefs)\n",
    "print('\\nAlpha值:',alpha_aic_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "."
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "前四个进入模型的自变量： ['Rating' 'Limit' 'Income' 'Student_Yes']\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "还可以使用sklearn的linear_model.lars_path()函数绘制正则化路径\n",
    "'''\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model\n",
    "X3=np.array(X2)\n",
    "Y3=np.array(Y2)\n",
    "_, n3, coefs = linear_model.lars_path(X3, Y3, method='lasso',verbose=True)\n",
    "xx = np.sum(np.abs(coefs.T), axis=1)\n",
    "xx /= xx[-1]\n",
    "plt.figure(figsize=[12,8])\n",
    "plt.plot(xx, coefs.T)\n",
    "ymin, ymax = plt.ylim()\n",
    "plt.vlines(xx, ymin, ymax, linestyle='dashed')\n",
    "plt.xlabel('|coef| / max|coef|')\n",
    "plt.ylabel('Coefficients')\n",
    "plt.title('LASSO Path')\n",
    "plt.axis('tight')\n",
    "plt.legend(np.array(predictors)[n3],fontsize=14)\n",
    "plt.show()\n",
    "print('前四个进入模型的自变量：',np.array(predictors)[n3[0:4]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 回归诊断<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4.1 三种残差\n",
    "普通残差、标准化（内学生化）残差、外学生化残差"
   ]
  },
  {
   "attachments": {
    "4.%E5%9B%9E%E5%BD%92%E5%88%86%E6%9E%905_1.jpg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### 例1：分析气压和温度之间的关系<br>\n",
    "\n",
    "![4.%E5%9B%9E%E5%BD%92%E5%88%86%E6%9E%905_1.jpg](attachment:4.%E5%9B%9E%E5%BD%92%E5%88%86%E6%9E%905_1.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\users\\hp\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\scipy\\stats\\stats.py:1542: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=17\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.995</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.995</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2965.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 16 Aug 2021</td> <th>  Prob (F-statistic):</th> <td>1.18e-18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:12:02</td>     <th>  Log-Likelihood:    </th> <td> -6.5592</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    17</td>      <th>  AIC:               </th> <td>   17.12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    15</td>      <th>  BIC:               </th> <td>   18.78</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -42.1309</td> <td>    3.339</td> <td>  -12.618</td> <td> 0.000</td> <td>  -49.248</td> <td>  -35.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X</th>         <td>    0.8955</td> <td>    0.016</td> <td>   54.450</td> <td> 0.000</td> <td>    0.860</td> <td>    0.931</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>38.307</td> <th>  Durbin-Watson:     </th> <td>   2.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  93.790</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 3.183</td> <th>  Prob(JB):          </th> <td>4.30e-21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>12.585</td> <th>  Cond. No.          </th> <td>7.38e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 7.38e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.995\n",
       "Model:                            OLS   Adj. R-squared:                  0.995\n",
       "Method:                 Least Squares   F-statistic:                     2965.\n",
       "Date:                Mon, 16 Aug 2021   Prob (F-statistic):           1.18e-18\n",
       "Time:                        12:12:02   Log-Likelihood:                -6.5592\n",
       "No. Observations:                  17   AIC:                             17.12\n",
       "Df Residuals:                      15   BIC:                             18.78\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    -42.1309      3.339    -12.618      0.000     -49.248     -35.014\n",
       "X              0.8955      0.016     54.450      0.000       0.860       0.931\n",
       "==============================================================================\n",
       "Omnibus:                       38.307   Durbin-Watson:                   2.022\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               93.790\n",
       "Skew:                           3.183   Prob(JB):                     4.30e-21\n",
       "Kurtosis:                      12.585   Cond. No.                     7.38e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 7.38e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "使用沸点的温度（F）作为自变量，使用气压取对数乘以100（log100）作为因变量构建回归模型\n",
    "'''\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.stats.api as sms\n",
    "import statsmodels.formula.api as smf\n",
    "data = np.array([194.5, 20.79, 1.3179, 131.79,\n",
    "                 194.3, 20.79, 1.3179, 131.79,\n",
    "                 197.9, 22.40, 1.3502, 135.02,\n",
    "                 198.4, 22.67, 1.3555, 135.55,\n",
    "                 199.4, 23.15, 1.3646, 136.46,\n",
    "                 199.9, 23.35, 1.3683, 136.83,\n",
    "                 200.9, 23.89, 1.3782, 137.82,\n",
    "                 201.1, 23.99, 1.3800, 138.00,\n",
    "                 201.4, 24.02, 1.3806, 138.06,\n",
    "                 201.3, 24.01, 1.3805, 138.05,\n",
    "                 203.6, 25.14, 1.4004, 140.04,\n",
    "                 204.6, 26.57, 1.4244, 142.44,\n",
    "                 209.5, 28.49, 1.4547, 145.47,\n",
    "                 208.6, 27.76, 1.4434, 144.34,\n",
    "                 210.7, 29.04, 1.4630, 146.30,\n",
    "                 211.9, 29.88, 1.4754, 147.54,\n",
    "                 212.2, 30.06, 1.4780, 147.80])\n",
    "###对数据进行预处理\n",
    "data = np.reshape(data,(17,4))\n",
    "data = pd.DataFrame(data,columns=(\"F\", \"h\", \"log\", \"log100\"))\n",
    "###formula公式\n",
    "formula = 'y~X'\n",
    "lm_ols = smf.ols(formula,data={'y':data.log100, 'X':data.F,})\n",
    "results1_1 = lm_ols.fit()\n",
    "results1_1.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### 三种残差：\n",
    "\n",
    " \n",
    "#####  (1)普通残差\n",
    "\n",
    "#####  (2)标准化(内学生化)残差\n",
    "\n",
    "#####  (3)外学生化残差\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型结果resid变量的值： [-0.24659031 -0.0674978  -0.06116289  0.02110585  0.03564332 -0.04208794\n",
      "  0.05244954  0.05335703 -0.15528173 -0.07573547 -0.14529928  1.35923819\n",
      "  0.00147182 -0.3226119  -0.24308321 -0.07763824 -0.08627699]\n",
      "\n",
      "通过公式计算的普通残差： [[-0.24659031 -0.0674978  -0.06116289  0.02110585  0.03564332 -0.04208794\n",
      "   0.05244954  0.05335703 -0.15528173 -0.07573547 -0.14529928  1.35923819\n",
      "   0.00147182 -0.3226119  -0.24308321 -0.07763824 -0.08627699]]\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "三种残差的计算\n",
    "'''\n",
    "###帽子矩阵的计算\n",
    "x = np.matrix(results1_1.model.wexog)##自变量设计矩阵\n",
    "###pinv函数计算Moore-Penrose伪逆矩阵。在矩阵是奇异矩阵，即非满秩矩阵时，矩阵没有逆矩阵\n",
    "#但是可以通过pinv计算其伪逆矩阵。\n",
    "H = np.dot(np.dot(x,np.linalg.pinv(np.dot(x.T,x))),x.T)\n",
    "\n",
    "res_model=results1_1.resid\n",
    "print('模型结果resid变量的值：',np.array(res_model))\n",
    "#单位矩阵I\n",
    "im=np.identity(H.shape[0])\n",
    "#获得因变量\n",
    "y=results1_1.model.wendog#或data.log100，或data['log100']\n",
    "###计算普通残差\n",
    "res=np.dot(im-H,y)\n",
    "print('\\n通过公式计算的普通残差：',np.array(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型结果的标准化残差： [-0.72468487 -0.19916136 -0.17081887  0.05864913  0.09821814 -0.11558547\n",
      "  0.14329707  0.14566162 -0.42347433 -0.20660753 -0.39545899  3.70795021\n",
      "  0.00418786 -0.90711019 -0.70502414 -0.23049112 -0.25782046]\n",
      "\n",
      "手工计算的标准化残差： [-0.72468487 -0.19916136 -0.17081887  0.05864913  0.09821814 -0.11558547\n",
      "  0.14329707  0.14566162 -0.42347433 -0.20660753 -0.39545899  3.70795021\n",
      "  0.00418786 -0.90711019 -0.70502414 -0.23049112 -0.25782046]\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "标准化残差(内学生化残差)\n",
    "'''\n",
    "###帽子矩阵的对角线元素\n",
    "hii=np.diag(H)\n",
    "'''\n",
    "###\n",
    "帽子矩阵对角线的另一种计算方法。\n",
    "'''\n",
    "\n",
    "###手工计算标准化残差\n",
    "#获得OLSInfluence对象\n",
    "influ1_1=results1_1.get_influence()\n",
    "#模型结果中的标准化残差\n",
    "inf_isres=influ1_1.resid_studentized_internal\n",
    "print('模型结果的标准化残差：',inf_isres)\n",
    "#计算残差标准差的无偏估计,res_model为普通残差，df_resid为残差自由度\n",
    "sigma=np.sqrt(np.sum(res_model**2)/results1_1.df_resid)\n",
    "#计算标准化（内学生化）残差\n",
    "isres=np.array(res_model/(sigma*(1-hii)**0.5))\n",
    "print('\\n手工计算的标准化残差：',isres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型结果的外学生化残差： [-7.12699486e-01 -1.92663060e-01 -1.65187452e-01  5.66669375e-02\n",
      "  9.49182692e-02 -1.11715932e-01  1.38532978e-01  1.40822128e-01\n",
      " -4.11582776e-01 -1.99886460e-01 -3.84056986e-01  1.24036925e+01\n",
      "  4.04585534e-03 -9.01424962e-01 -6.92691672e-01 -2.23070953e-01\n",
      " -2.49631950e-01]\n",
      "\n",
      "手工计算的外学生化残差： [-7.12699486e-01 -1.92663060e-01 -1.65187452e-01  5.66669375e-02\n",
      "  9.49182692e-02 -1.11715932e-01  1.38532978e-01  1.40822128e-01\n",
      " -4.11582776e-01 -1.99886460e-01 -3.84056986e-01  1.24036925e+01\n",
      "  4.04585534e-03 -9.01424962e-01 -6.92691672e-01 -2.23070953e-01\n",
      " -2.49631950e-01]\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "外学生化残差的计算，用looo方式计算外学生化残差\n",
    "'''\n",
    "###模型的外学生化残差\n",
    "inf_esres=influ1_1.resid_studentized_external\n",
    "print('模型结果的外学生化残差：',np.array(inf_esres))\n",
    "###手工计算的外标准化残差（使用公式计算的结果和StatsModels一致）\n",
    "#res_model为普通残差\n",
    "esres=res_model*((results1_1.df_resid-1)/(np.sum(res_model**2)*(1-hii)-res_model**2))**0.5\n",
    "print('\\n手工计算的外学生化残差：',np.array(esres))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4.2 残差图\n",
    "对模型进行诊断的重要工具\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (1) 以回归值$\\hat{y}$作为横坐标的残差图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "y_hat=results1_1.predict()#获取因变量的回归值\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.scatter(y_hat,isres)#残差图\n",
    "\n",
    "#画出参考线y=0\n",
    "plt.axhline(y=0,alpha=0.6,color='red')\n",
    "expt_index=np.argmax(np.abs(np.array(isres)))\n",
    "ex=y_hat[expt_index]\n",
    "\n",
    "#标出离群点\n",
    "plt.text(ex+0.3,isres[expt_index],str(expt_index),c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\users\\hp\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\scipy\\stats\\stats.py:1542: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>3.266e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 16 Aug 2021</td> <th>  Prob (F-statistic):</th> <td>5.55e-25</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:12:02</td>     <th>  Log-Likelihood:    </th> <td>  13.214</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    16</td>      <th>  AIC:               </th> <td>  -22.43</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    14</td>      <th>  BIC:               </th> <td>  -20.88</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -41.3018</td> <td>    1.000</td> <td>  -41.286</td> <td> 0.000</td> <td>  -43.447</td> <td>  -39.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X</th>         <td>    0.8910</td> <td>    0.005</td> <td>  180.734</td> <td> 0.000</td> <td>    0.880</td> <td>    0.902</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.645</td> <th>  Durbin-Watson:     </th> <td>   1.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.439</td> <th>  Jarque-Bera (JB):  </th> <td>   1.316</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.630</td> <th>  Prob(JB):          </th> <td>   0.518</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.377</td> <th>  Cond. No.          </th> <td>7.17e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 7.17e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       1.000\n",
       "Model:                            OLS   Adj. R-squared:                  1.000\n",
       "Method:                 Least Squares   F-statistic:                 3.266e+04\n",
       "Date:                Mon, 16 Aug 2021   Prob (F-statistic):           5.55e-25\n",
       "Time:                        12:12:02   Log-Likelihood:                 13.214\n",
       "No. Observations:                  16   AIC:                            -22.43\n",
       "Df Residuals:                      14   BIC:                            -20.88\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    -41.3018      1.000    -41.286      0.000     -43.447     -39.156\n",
       "X              0.8910      0.005    180.734      0.000       0.880       0.902\n",
       "==============================================================================\n",
       "Omnibus:                        1.645   Durbin-Watson:                   1.575\n",
       "Prob(Omnibus):                  0.439   Jarque-Bera (JB):                1.316\n",
       "Skew:                          -0.630   Prob(JB):                        0.518\n",
       "Kurtosis:                       2.377   Cond. No.                     7.17e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 7.17e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "去掉第11条（下标从0开始）数据后再进行拟合。\n",
    "'''\n",
    "data_F = data.F[np.arange(0,len(data.F))!=11]\n",
    "data_log100 = data.log100[np.arange(0,len(data.log100))!=11]\n",
    "results1_2 = smf.ols(formula,data={'X':data_F,'y':data_log100}).fit()\n",
    "res_except11 = results1_2.resid\n",
    "results1_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "去掉第11个数据点之前的残差正态性检验： ShapiroResult(statistic=0.546539306640625, pvalue=3.3020596674759872e-06)\n",
      "去掉第11个数据点之后的残差正态性检验： ShapiroResult(statistic=0.9221550226211548, pvalue=0.18269383907318115)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "残差正态性的夏皮罗检验\n",
    "'''\n",
    "###夏皮罗检验(shapiro)函数的原假设是：被检验样本数据符合正态分布\n",
    "print('去掉第11个数据点之前的残差正态性检验：',st.shapiro(res_model))\n",
    "print('去掉第11个数据点之后的残差正态性检验：',st.shapiro(res_except11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 去除异常数据的标准化残差图\n",
    "influ1_2=results1_2.get_influence()\n",
    "y_hat2=results1_2.predict()#获取因变量的回归值\n",
    "isres2=influ1_2.resid_studentized_internal\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.scatter(y_hat2,isres2)#残差图\n",
    "#画出参考线y=0\n",
    "plt.axhline(y=0,alpha=0.6,color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "使用外学生化残差图试试\n",
    "'''\n",
    "\n",
    "esres2=influ1_2.resid_studentized_external\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.scatter(y_hat2,esres2)#残差图\n",
    "#画出参考线y=0\n",
    "plt.axhline(y=0,alpha=0.6,color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (2) 残差QQ图\n",
    "根据前面章节中介绍的QQ图原理，样本数据QQ图上的点如果大致分布在一条直线，那么该数据符合正态分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9lCZL1dWlSwsuW5YuDH7eeSnot97ak6ZKrK2LqDzT5OGaEdEwRr8fMAn4ZTELM7Mq9cknaXZsXV26WPiSJTBkCJx+egr6sWMd9GVU6Inc/5V0L7AOsDFwSdEqMrPqs2LFZ2fHvv8+9O8Pxx+fgn733T07tkIUGvoTgL1JV7r6ICKWFa8kM6sK9fVpieKG2bHz58Naa6XZsTU1aajlaquVu0prptDQXwr0iIgFAJK2iohni1eWmVWkCHj88cbZsW+8kWbHHnpoCvqDDvLs2ApXaOjvCfyrpMWk0TtrAZ4OZ5YXzz7bODv2pZdSC/7AA+H730/LIXh2bNUoNPTPA56IiADIJmmZWXf28suNs2Offjr1ye+9N5x/furC8ezYqrTK0Je0D3AWMFzSHODHEXFn0Sszs9J7883Ga8c2zI7dbTf48Y/TJQU9O7bqtRn6kiYCxwPXAv8gXfjkJEn9I+LaEtRnZsU2fz7ceGPj7NgIGD0arrwyzY71daq7lVW19LeMiEOabauTdH6xCjKzEnj/ffjjH1PQ3313mh07ahRcfHFarnjzzctdoRXJqkL/9Va2d/hSiWZWJkuWwC23pKC/7bY0O3bECDj33DTyZtttPWkqB1YV+ltL6hMRnzRskNQP2La4ZZlZl/jkE7jrrhT0M2emZREGD4bTTktBP26cgz5nVhX604GnJD0CvEGanDUe+EqR6zKzjlqxAu67LwX9TTelhc7WXx8mT05Bv8ce0NML5eZVm6EfEbMl7U0K+aHAK8B3I2JeKYozswLV18PDD6fhlddfD++8k8bOH3lkCvr99vPsWAMKGLIZEW8B3y9BLWbWHhHwxBONs2Pnzk2zYQ85JI26mTAhzZY1a8KXjTerNs891zg79sUXoVevdHHwqVPh8MPT+jdmrXDom1WDV19tnB371FPp5Otee8E556TZsf37l7tCqxIOfbNK9dZbafXKujp45JG0bZdd4Ic/TLNjBw8ub31WlbzAtVklefddmDYtrXGz8cbwzW/Cxx/D976XWvuzZsEZZ3T7wJ8+PU0h6NEj/Zw+vbo+v5K5pW9Wbh98ADNmpBb9XXelIZebbw7f+U46IbvFFuWusKSmT4cpU9JcMoA5c9JjSKNOK/3zK52yhTMr1pgxY2L27NnlLsOsay1dCrfemoL+1ltTa37YsDS8sqYGtt++6iZNTZ8OF12UBhENG5bOK3ckREeMSEHc3PDh8Nprna2y+J9fKSQ9HhFjmm93945ZqSxblgL+K1+BgQNTv/yDD8LJJ6crUL36alqffocdSh74ne3uaGg9z5mTRpI2tJ470m0yd277tlfa51c6d++YFdPKlXD//alFf+ONsHBhWoe+oUW/555pyGUZdUV3x0UXNb6/wZIlaXt7W/vDhrXcEu+qxT6L/fmVzi19s64WkWbHnnlmOhm7zz5w7bXpSlM33wzz5sEvfpG2lznwoe3ALlRXtp6nToV+/T67rV+/tL0rFPvzK13JQ19SL0mXSTpS0oWS/IvHql8EPPlkuqrUJpvArrvCz3+efjYsizB9epot27t30croSDdNVwR2a63kjrSeJ09OA5iGD0+9XMOHp8dddZK12J9f8SKipDfgNODU7P6pwLFtvX7HHXcMs4r1/PMRl1wSMWpUBET07Blx0EERv/lNxKJFJS3ld7+L6NcvldFw69cvbW/L8OGffU/Dbfjw4u/bigeYHS1kajla2TsDT2b3nwImlKEGs46bMyddVWr06HThke9+FzbcEH72s9R1c9ttcPzxsM46JS2ro900XdHdkfvWcxUpR4fihsCH2f0Pgc9ddFPSFGAKwLC8nF2xyjZvXuPs2Fmz0raddoJrrkmjcDbaqLz10fFumoZg7uxwy8mTHfLVoBwt/XeBNbP7awILmr8gIqZFxJiIGDNgwICSFmf2qYUL4Ze/hH33TaF+xhmweDFcfjm8/DI8+miaMVukwG9v/3xn+tUnT05j1Ovr00+Hd/dVjpb+ncB2wKOkK3DdVYYazFr24YfpClN1dXDnnbB8OWy2WWoG19TAlluWpIyODKOcOvWz74F8jUqxwpSjpf9bYJikiaQLs/yuDDWYNVq6NF1hauJEGDQIjjsujcQ580yYPTstX3zppZ0K/Pa22jvSP+9+dSuEl2GwfFq+HO6+O7Xo//CH1MJvmCVbU5OGWvZouU3U3uUGmrfaIbXA2wrkHj3SGJjmpNQFY7YqXobBbOXKdO3YU09Nq1QefDDMmMFLo4/huIF3sdo7bzLilp8wfc6X2gz89i430JFWe1eOezdryqFv3crnulF+F+mE61lnpcTcay/47W9h//1hxgxq/+MfbPfYr5j+zn6soNcqQ7wjAd6RUTV5nzVqRdTS4P1KunlyVj797ndpcpCUfhYyyadxglB9bMNTMZUL4hVtkmYK9e4dccQREXV1EYsXf/qe9k5Mklp+vdR6XR2d/NSRY2DWgFYmZ5U91Fd1c+jnT0dnd+455MX4NpfGM2wZAbGcnnE7B8S3+v+61dmx7Q3xjgS4Z6taOTj0rWq0K1jnzIm46qqI0aMjIFaiuJ/d41T+MzbgnS5vhXc0wN1qt1Jz6FvVWGXre968iJ/8JGK33RqfHDs2Ll3v32MjXi96K9wBbtWgtdAv/7quZs20tN75urzHyevdBPvVwZ//nMYtbr11OrN57LEwciSbTof3pgDtmJzUkSUIvNyAVTOP3rGi6shSvw0jV9ZgMZO4lhkcxj8YxJULv56uLnXhhfD00+l24YUwciTQ8clJXoLAcqWl5n8l3dy9U16d6croUP/30qURN90Ur42bGEu0egTEmz03jmcP/lbEY49F1Nd38huZ5QOtdO94Rq61qiMzSZsq+ALUy5fDPfc0zo794AMYMKBxduxuu7U6WcrMWtbajFyHvrWq4NBuRZtLCayohwceSEF/ww2wYEFaf/6oo1LQ7713RVxK0KxatRb6/ldlrersZfQ+f0I2GMNsTlmzFoZdD2++mf50OOywFPQHHgh9+nS2bDNrg0PfWtXSKJqG7YVoWOp3kyXPMIlaaqhjJK+wcmlv2Psg+MEP4NBDYY01urZwM2uVQ99a1an12V96icmv1jFh/TrWXfIsK+jJrL778M5Xvs0uVx4J665brLLNrA0OfWtVu8ewv/46XH996qfPzsOsu/vucMFP6XX00ewxcGBpCjezVjn0rU2rnIj0zjvpRGxdXToxCzBmTOq6mTgRhg4tSZ1mVhiHvrXfokVpaGVdXRpquXJluqrUZZel2bFf+EK5KzSzVjj0rTAffQQ335yC/vbbYdky2HRTOO+8NPJmm23KXaGZFcChb6375BO4444U9DNnpjO6Q4bA6afDpEmpG0cqd5Vm1g6e5tgNdWS9m0+tWAF33QUnnpguEn7EEfCnP8Hxx6dLDb7+Olx9NYwd68A3q0IO/RLrVCAX+PntvYYr9dns2NNPTy35Aw6AG29MgX/HHfD22/Bf/wV77unlEMyqnJdhKKHOrmVTiIKXToiAxx9PXTfXXQdvvAGrr54mS02alGbH9u3bNUWZWcl57Z0K0Nm1bArR5no39cCzz6agr6uDl16C1VZLAV9Tk5ZDWHPNrinEzMrKa+9UgM6uZVOIlpZO2JSXOWWd62DburQGfY8eaUGz889PC5ytt17XFWBmFc2hX0KdXcumEA1LJ6y75E0mcj011DGOv8IiYKvd4Mc/TksWDxrUdTs1s6rhs3Il1HBFqKYKXsumEAsWMPnDn/HKsPG8zlCu4Wz69V7B32quTL9tHnwQvvENB75ZjrmlX0IduR7rKr3/Pvzxj6mP/k9/gpUrGTRqFFxyMdTUsM0Xv9gVpZtZN9EtW/rFHhbZGV1yPdYlS9LCZkcdlVrtJ5wAzz8P554LTz4Jf/87XHwxOPDNrJlu19JvPiyyYZw6VPkFr5ctgzvvTC36GTPSsgiDB8Opp6aRN+PGebKUma1StxuyWYphkSWzYkWaBVtXlyZLLVoE668PRx+dgn6PPaBnz3JXaWYVqKKGbEraPiKeLMZnl2JYZFHV18PDD6egv/76tHTxWmul2bGTJsG++6ax9WZmHVDy0Jd0CPALYHAxPr8UwyK7XAQ88UTj7Ni5c9Ns2EMOSUF/0EFptqyZWSeVPPQj4hapeJ3PnbrEX6k991zj7NgXX4RevdK6N1OnwuGHpxa+mVkX6nYncosyLLIrvfpqas3X1sL//m86+brXXnDOOWk0Tv/+5a7QzLqxspzIlTQvIjZs4/kpwBSAYcOG7Tinpf6aavLWW/D736egf/TRtG3nnVPXzTHHpFE4ZmZdqKQnciUdCJzfwlOnRMQLq3p/REwDpkEavdPF5ZXGu++mETe1tXD//anffvvt4XvfS5cUHDGi3BWaWQ4VJfQj4g7gjmJ8dkX74IM0hr62Ns2OXbECNt8cvvOdNMRy1KhyV2hmOVeO0TsHA+tJGhsRj5V6/11u6VK49dZ0MvbWW+Hjj9OJhLPPTt03223nSVNmVjHKMXrnNqBPqffbpZYtSy352trUsl+8OC2HcPLJKeh33tlBb2YVqduN3imalStT33xtbeqrf++9tA59TU26jR/v2bFmVvEc+m2JgEceaZwdO28erLFGmh1bUwP77w+9e5e7SjOzgjn0m4uAp55qnDQ1Zw706QMTJqSgnzDh84vim5lVCYd+gxdeaAz6559Ps2P32w8uvTS17Ndeu9wVmpl1Wr5Df86cxtmxTz6ZTr7uuSecdVaaHbvBBuWu0MysS+Uv9OfNa5wd+/DDadu4cXDNNTBxIgwZUt76zMyKKB+hv3Ah3HRTCvr77kvLF2+7LVx+eZodu+mm5a7QzKwkum/of/ghzJyZ+ujvvBOWL4fNNksrsdXUwJZblrtCM7OS656hHwE77AAvvwxDh8KZZ6ZJUzvs4ElTZpZr3TP0pbSw2YYbwq67piukm5lZNw19SNeRNTOzz3AT2MwsRxz6ZmY54tA3M8sRh76ZWY449M3McsShb2aWIw59M7McceibmeWIIqLcNbRJ0nxgTrnraGYDYEG5iyhQNdUK1VVvNdUK1VWva+284RExoPnGig/9SiRpdkSMKXcdhaimWqG66q2mWqG66nWtxePuHTOzHHHom5nliEO/Y6aVu4B2qKZaobrqraZaobrqda1F4j59M7MccUvfzCxHHPrtJGn7Al6zdglKWaVCaq02lXJsu4NKOpaStpHUs9x1FGJVtVbScW2JQ78dJB0C3N7Kc1tKekXSS8A/lbayFutpq9YaSSdKOl/SyBKX1lI9a0uaKulISWe38HxZj62kXpIuy+q7UFKPJs/tLekMSWdKGlfq2lqyinr7S3ohO5bnlrHMT0naGXgEWK3Z9ko8tq3VWnHHtVUR4Vs7bsC8VrZfBgwud32rqhVYH7g3u78WMLMC6vw+cGB2/3vAuEo6tsBpwKnZ/VOBY7P7PYHZgLLbPeU+lm3Vmz0+BxhV7hpbqPk1oG+TxxV5bFuqtZKPa0s3t/S7zlDgMUk3Supb7mLasB3wAkBEfAhsJmm1tt9SdDsDT2b3nwImNHu+3Me2tfqGAQsiA6yQtGkZ6muureM5ALhF0n2S+pe6sHao1GPbmmo5rg79rhIRJwBfAOqBb5S3mjZtCHzY5PES0jTycmpa04fAoKZPVsCxba2+5sfyc7WXSavHMyLOA75I+qXw3ZJXVrhKPbYtqqLj2o0vjN5Bkg4Ezm/hqVMi4oW23hsRSyVdAUwpSnHNdLDWd4E1mzzuByzs6tpa0ka9a2c1fZT9/Nw6JqU+ts00PWZN62t+LFusvQxaqxeAiFgp6VLg2lIX1g6VemxbVSXH1aHfXETcAdxRyGsl9Sb9z/gesFpELAMGkk70FF0Ha32I7ESTpDWBNyLik6IV2URr9Uq6mNTtdBewLXBnuY9tM3dm9T2a1XeXpIER8aKktSQpe92aEfF/ZaivudbqfUdSn+y/d7mOZZuyk84bVPCx/VSTWiv+uDbl7p12kHQwsJ6ksdmmA4CLgF2Av0r6OrAR8Jsylfip1mqNiI+AaZKmkE4+nVGuGpu4CthH0rHAhxFxP5V1bH8LDJM0kXR+4RngJ9lzFwDfym4XlKG2lrRYr6RNgMclnQGMB64oX4mNJI0h9YnvT/olVbHHtqVaK/W4tsYzcs3McsQtfTOzHHHom5nliEPfzCxHHPpmZjni0LeqIWmspJPKXUdT2bpA/09SSYc/KzlU0nGl3K9VP4e+VRxJd0m6JPt5l6SLJT0ELAW+UqR99pG0VnZ/Rjum0r9EGvZa1NCXNCD7uaukK7OlCXoBhxRzv9b9OPStEk2NiEuAWcCsiPguacz+4iLu88ekeQAAEyPi3ULelE0a+6hoVQGStgD+LdvfLODb2VPvFXO/1j15Rq5VnGxyVvNt90kaAawr6QJgInAoaQmJycDqwDYRcXK23MP6wAjSdP464EfAi8Bh2fsOAwaT/g38CPgS8Lykt4GJkv4SES9IOhF4HzgeOAHYC+gPbA38R0S82tJ3kHQYaXZmP2AccCZpNvThwBjg6xExXtI3gHmkyWjnAEeTFkh7LLu/O2nCzzaSdgFWkiYF/bLZ/vbN9ndw9n3eAb4M9CH94ryv9SNueeKWvlWdiLgCuI8U1CcC65BCrrekQcC3I+LaiLgc+GdgDdLyvK+QZvj+K7Ac+D/SIlnvZe+/jdRqPxAYLGk8sCwibgR+RVrudw3gj8Cr2f4/J1ve4rSI+CVwCzAyIhYAb2cvmd3sLTNIf8VsC7zc5DvOJS2n8BwwNyIezur+TD9+thzAGaQF6Z4Gxmbv24i0VHVFLV9g5eWWvlWbj7OfnwB9ga2An0bEM0CdpIGkVn6D54CRpEB8LyLqJW0FXBIRi0l/BdCwxEtErJDUsLrjGOD5bPvM7HUPkvrR1wAWtVLjFsCy7H79Kr7PQ8AxpF8oDVdjanjvJ6SWelMtdekMIP1yqmvYkJ1YPhL4C3DsKmqwHHFL36rd68BXIY2kAdYDVpe0XvZ8P7LgbuU9X5K0ASmcm/97eAOYnI2UGZh1L/2G9BfBfNJfDy15F9ghu05B04bVSlI3VNNrAtRmt8VtfF5LtTW1ENhV0uDsOx1J6uo6Afg1cFYb77WccehbRcoCbCwwVtLQbPNoUrfLRsAoUnfINGAXSQ8AB2RLSk8Bvi2phhSoK0jr8e+UBfH3gZMl3Q2MyLpeHgHOyT5702zfN5Ja2k+RrkT1GukXxk9JXUrHSNqOdD2CHRtqj4hXSJeqvJ3Uh9/gZuDK7LPrsxE5C7LvUA98jdQtMyL7/puR/pJ5gdSnfxiwE7CRpCHAbqTzFmsDlwAPSKrN6t0uO/exNq1cNtPyyQuumRVR9tdBXUTsXO5azMAtfbNiGw8Mahhnb1ZubumbmeWIW/pmZjni0DczyxGHvplZjjj0zcxyxKFvZpYjDn0zsxz5/y8VRB158yr4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\AppData\\Roaming\\Python\\Python37\\site-packages\\statsmodels\\graphics\\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bo\" (-> marker='o'). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n",
      "C:\\Users\\hp\\AppData\\Roaming\\Python\\Python37\\site-packages\\statsmodels\\graphics\\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bo\" (-> marker='o'). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "残差QQ图\n",
    "'''\n",
    "### 去除离群点前的残差QQ图\n",
    "st.probplot(isres,plot=plt)\n",
    "plt.figure()\n",
    "\n",
    "###去除离群点后的模型残差QQ图\n",
    "st.probplot(isres2,plot=plt)\n",
    "plt.show()\n",
    "\n",
    "### 还可以使用StatsModels的qqplot函数绘制QQ图\n",
    "#注意line参数的用法\n",
    "import statsmodels.api as sm\n",
    "sm.qqplot(isres,line='r')\n",
    "sm.qqplot(isres2,line='r')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4.3 影响分析\n",
    "探查对模型的回归估计有异常大影响的数据。样本中有一个数据不遵从某个模型，但是其他数据都遵从，则该样本点是强影响点。上节中通过残差可以探测离群点，下面介绍其他方法。主要关注两类异常数据：杠杆点和强影响点<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Statsmodels的影响分析相关数据放在OLSInfluence对象里\n",
    "通过模型的结果对象获取，详见前文。\n",
    "'''\n",
    "#影响分析\n",
    "influ1_1.plot_influence()\n",
    "plt.ylim(-2,6)\n",
    "plt.xlim(0.04,0.23)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (1) DFFITS准则\n",
    "模式预测值差异量\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型计算dffits值： [-3.49029139e-01 -9.62971659e-02 -5.71578858e-02  1.86654709e-02\n",
      "  2.84824973e-02 -3.21268857e-02  3.70533931e-02  3.72188699e-02\n",
      " -1.07054336e-01 -5.22553169e-02 -9.66963947e-02  3.24164490e+00\n",
      "  1.62954461e-03 -3.31142340e-01 -3.15595317e-01 -1.14885574e-01\n",
      " -1.32545561e-01] \n",
      "模型阈值： 0.6859943405700354\n",
      "\n",
      "手工计算dffits：\n",
      " [-3.49029139e-01 -9.62971659e-02 -5.71578858e-02  1.86654709e-02\n",
      "  2.84824973e-02 -3.21268857e-02  3.70533931e-02  3.72188699e-02\n",
      " -1.07054336e-01 -5.22553169e-02 -9.66963947e-02  3.24164490e+00\n",
      "  1.62954461e-03 -3.31142340e-01 -3.15595317e-01 -1.14885574e-01\n",
      " -1.32545561e-01]\n",
      "手工计算阈值： 0.6859943405700354\n",
      "\n",
      "按照阈值，DFFITS统计量超过标准的数据点是 [11]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 直接从OLSInfluence对象例取得该值。\n",
    "dffits = influ1_1.dffits\n",
    "print('模型计算dffits值：',dffits[0],'\\n模型阈值：', dffits[1])\n",
    "\n",
    "'''\n",
    "使用公式手工计算\n",
    "'''\n",
    "dffits1 = esres* np.sqrt(hii / (1 - hii))\n",
    "threshold_dffits=2*(2/17)**.5\n",
    "print('\\n手工计算dffits：\\n',np.array(dffits1))\n",
    "print('手工计算阈值：',threshold_dffits)\n",
    "print('\\n按照阈值，DFFITS统计量超过标准的数据点是',(np.where(np.abs(dffits[0]) > dffits[1]))[0])\n",
    "plt.scatter(np.arange(len(dffits[0])),dffits[0])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (2) Cook统计量\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型库克距离：\n",
      " (array([6.29765505e-02, 4.95461931e-03, 1.74678678e-03, 1.86599953e-04,\n",
      "       4.34320137e-04, 5.52437945e-04, 7.34504173e-04, 7.41045465e-04,\n",
      "       6.06622216e-03, 1.45866825e-03, 4.95680839e-03, 4.69533612e-01,\n",
      "       1.42254243e-06, 5.55213941e-02, 5.15892420e-02, 7.04568686e-03,\n",
      "       9.36989623e-03]), array([0.93921241, 0.99505926, 0.99825494, 0.99981342, 0.99956579,\n",
      "       0.99944773, 0.9992658 , 0.99925927, 0.99395458, 0.99854254,\n",
      "       0.99505709, 0.6341802 , 0.99999858, 0.94618526, 0.94988665,\n",
      "       0.99298236, 0.99067966]))\n",
      "\n",
      "手工库克距离为：\n",
      " [6.29765505e-02 4.95461931e-03 1.74678678e-03 1.86599953e-04\n",
      " 4.34320137e-04 5.52437945e-04 7.34504173e-04 7.41045465e-04\n",
      " 6.06622216e-03 1.45866826e-03 4.95680839e-03 4.69533612e-01\n",
      " 1.42254243e-06 5.55213942e-02 5.15892420e-02 7.04568686e-03\n",
      " 9.36989623e-03]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cook距离统计量最高的数据点是: 11\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Cook距离。\n",
    "'''\n",
    "cook1 = influ1_1.cooks_distance\n",
    "cook2 = (1/2)*(hii/(1-hii))*(isres**2)\n",
    "print('模型库克距离：\\n',cook1)\n",
    "print('\\n手工库克距离为：\\n',cook2)\n",
    "plt.scatter(range(len(cook2)),cook2)\n",
    "plt.show()\n",
    "##使用np.argmax函数\n",
    "print('Cook距离统计量最高的数据点是:',np.argmax(cook2))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (3) COVRATIO准则\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COVRATIO准则统计量最高的数据点是: 11\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "COVRATIO准则\n",
    "'''\n",
    "cr = influ1_1.cov_ratio\n",
    "### 根据COVRATIO准则，离1最远的那个值所对应的数据点为强影响点。\n",
    "top_k_idx=np.argmax(np.abs(cr-1))\n",
    "print('COVRATIO准则统计量最高的数据点是:',top_k_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.32536187, 1.42716636, 1.28040677, 1.27192597, 1.24971744,\n",
       "        1.24068271, 1.2267188 , 1.22467554, 1.19649296, 1.21944392,\n",
       "        1.19540813, 0.0085315 , 1.33418118, 1.16385341, 1.29589929,\n",
       "        1.44217786, 1.4585813 ]),\n",
       " array([1.32536187, 1.42716636, 1.28040677, 1.27192597, 1.24971744,\n",
       "        1.24068271, 1.2267188 , 1.22467554, 1.19649296, 1.21944392,\n",
       "        1.19540813, 0.0085315 , 1.33418118, 1.16385341, 1.29589929,\n",
       "        1.44217786, 1.4585813 ]))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###根据外学生化残差推导公式和简化计算公式反推looo残差方差向量\n",
    "\n",
    "sigma2_i=influ1_1.sigma2_not_obsi\n",
    "\n",
    "###残差方差\n",
    "sigma2=(np.sum(res_model**2)/results1_1.df_resid)\n",
    "###根据COVRATIO准则公式计算COVRATIO值,和cr对比\n",
    "np.array((sigma2_i**2/sigma2**2)*(1/(1-hii))),cr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (4) 其他影响衡量指标\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dfbetas：\n",
      " [[-0.29635088  0.29116408]\n",
      " [-0.08230325  0.08089618]\n",
      " [-0.03948936  0.03833712]\n",
      " [ 0.01218424 -0.01179046]\n",
      " [ 0.01593845 -0.01528276]\n",
      " [-0.01617394  0.01540386]\n",
      " [ 0.01373074 -0.01277838]\n",
      " [ 0.01268266 -0.01171484]\n",
      " [-0.031494    0.02866614]\n",
      " [-0.01619652  0.01482306]\n",
      " [ 0.00847536 -0.01112315]\n",
      " [-0.83061209  0.91653434]\n",
      " [-0.00120991  0.00123949]\n",
      " [ 0.22888737 -0.23538667]\n",
      " [ 0.25078446 -0.25596239]\n",
      " [ 0.09573156 -0.09744334]\n",
      " [ 0.1115131  -0.11344268]]\n",
      "\n",
      "根据dfbetas值，强影响数据点为 [11]\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "dfbetas：回归系数的标准化差分值或估计值差异量\n",
    "'''\n",
    "print('dfbetas：\\n',np.array(influ1_1.dfbetas))\n",
    "print('\\n根据dfbetas值，强影响数据点为',np.where(influ1_1.dfbetas>2*np.sqrt(1/17))[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BCyT1JfUFvho4takDI2IdcH6yemvyOiJ53fuzR5iZWbFkM0PZ5cCfJU0GHgH+TWqqyrsLE5qZmRXDBmsEkrqTepREpmXAPcAtwFdINQ19ryDRmZlZwTXWNLQ1cF8j+49OXgMnAjOzVmuDTUMRsQC4MCLaNfYDjCleuGZmlm9N9RFclrlB0o6SDpDUI9n0h/yHZWZmxdJoIoiIuvplSZ0k/QV4jlQfwauSfk/qMRFmZtZKZTOO4GdAF1J9A58H9gQWAxcVIC4zMyuSbMYRbBoR+2dsmyfpJ/kMyMzMiiubGsHDG9jePh+BmJlZaWSTCHZIHisBgKQqSaeSaiIyM7NWKpumoduA5yUtADoD/YAOwJcLEZiZmRVHNo+YeAT4KrCI1OjiqcCAiHiiMKGZmVkxZFMjICKekXQ50Bt4LiKWFCQqMzMrmmbXCDLGEdwNLJb0e0kbFyw6MzMruFzHEQwAhuBxBGZmrV5LxxHM9TgCM7PWzeMIzMwqnMcRmJlVOI8jMDOrcB5HYGZW4bIeR0DabGSSNsp7RGZmVlSN1ggkvZT8TJXUP2NfO+BSJwMzs9atqaahPsC0iDg+Ip5L3xER64DJwHcKFJuZmRVBU4ngVeDHG9oZEU8BO+U1IjMzK6qmEsH9EbG2ifdskq9gzMys+JpKBM25q6hH028xM7Ny1WQfQfogskyStsSJwMysVWsqEdwH/GF9TxiVtAlwA3BvAeIyM7MiaWocwQTgMVIjim8CXkyO2RkYAWwEnFLQCM3MrKAaTQQRsVzSgcAtpO4eimSXgFeAwyOitqARmplZQTU5sjgiXgL2kDQUGAxUAc8AMyPiowLHZ2ZmBdbsR0xExBxgTgFjMTOzEsjmMdRmZtYGlUUikNRe0s6ljsPMrBIVJRFIqpZ0saQjJf0keWBd/b6uwG+BE4sRi5mZfVqxagSnAUsi4nZScxkcW78jIlYA/ypSHGZmlqFYiWAv4Ilk+UngsCJ9rpmZNaFYiWBrYFWyvArYKpuDJY2UNFfS3LfffjvvwZmZVbJiJYJ3gJpkuQZYms3BETE5IgZHxODu3bvnPTgzs0pWrEQwAxiULA8EZiYPrDMzsxIrViK4EegtaQSwLfAUMBFA0qbAPsAgSVk1GZmZWctlNXl9rpJpLc9PVm9NXkck+1YCIwsdw4033siqVatYunQpQ4YMYdiwYYX+SDOzVqEoiaDUFi5cyNSpU5kxYwbr1q1j8ODBDB06lI4dO5Y6NDOzkiuLkcWFNnXqVPbdd18A2rVrx8CBA5k+fXppgzIzKxMVkQgWLlxIr169GtZ79erFU089VcKIzCyfamtrmTFjRsP6hAkTGDp0KP379+fRRx8tYWStQ0Ukgrq6Ojp1+mTGzZqaGpYuzeoOVjMrU/Pnz2fEiBHccccdADzyyCMcfPDBzJkzhzFjxjB+/PisystMKvWuuOIKrr/++nyEXHYqIhF069aNurq6hvW6ujq6detWwojMLF8GDhzIV7/61Yb1QYMGMWhQ6m71bt26sf322ze7rMykUq+2tpYpU6bkJ+AyVBGJYNCgQdTWfjKRWm1tLQMGDChhRGZWKBtvnJpifenSpdx8881ceOGFzT42M6nUu+GGGzjyyCPzFmO5qYhEcPTRR/Ovf6WeaxcRzJs3j8MPP7zEUZlZoaxatYqrrrqK+fPnc+KJLXuw8d13382BBx5IVVVVnqIrPxWRCJ55v4ZFm+zK5gedwXbDTuXQE8fQpUuXUodlZgXSuXNnxo4dy7x585g9ezYrV67MqZwPPviA+fPns8cee+Q5wvLS5scRTH98CedOW8DH/Q6gc/22ZVUMeXwJw3frWdLYzKywunXrxs4775zz1fy1117Lqaeemueoyk+bTwQTZjzH6jVrP7Vt9Zq1TJjxnBOBWRu0fPlyqqqq6NKlC++99x59+/alpqam6QPX49Zbb23oJH7jjTdo3749ixcvzqrfoTVo84mgdsXqrLabWevy/PPPM2vWLNasWcPTTz/NQw89xEUXXcSYMWPo3Lkzl19+ec5lz549u2F53Lhx9OnTh5NOOqnlQZeZNp8IenTtyJL1fOn36OrHS5i1BQvrOvHxoWN5fcVqRt7xJmcNO4T//Ce35pzMpLLrrrvmOdry1OYTwVnD+nPutAWfah7quFEVZw3rX8KozCwf6vsA6/++l6xYzbnTFgDk1PT7maTyUdeGcsaNG5e3uMtNm79raPhuPfn5UQPo2bUjAnp27cjPjxrg/gGzNqCxPsBs1SeVJStWE3ySVKY/viRP0ZavNl8jgFQy8Be/WduTzz7ASr6xpM3XCMys7dpQX18ufYCVfGOJE4GZtVpnDetPx40+PUYg1z7AfCaV1saJwMxarXz2AeYzqbQ2FdFHYGZtV776AOvLmDDjOWpXrKZH146cNax/m+8fACcCM7MGlXpjiZuGzMxasbVr1/L444+zfPnynMtwIjAzK4E1a9Zk9VTU9c2cNmXKFE4//XRqamrYbLPNco7FicDMrEAyv7xffPFF2rVrhyR69OjRMIlOU9Y3c9oVV1zBP/7xD6ZMmULfvn1bFKf7CHJUW1vLggULGDZsWKlDsTIwZcoUqqqqeP3119lss80YNWpUqUNqkHmu3njjjaxatYqlS5cyZMgQn8MFMn/+fEaNGsUXvvCFht/xLbfcwrJly+jQoQPV1dVUVzfvK7h+5rT6udZra2s555xzeOGFF5DU4lhdI8jBhuY1zcV5553HFltswY477sisWbPyEF15y7Y6vCH5aBfNl5deeomZM2dyyimncN5553HNNdewatWqUocFfPZcXbhwIVOnTmXUqFGcf/75nHvuuaxe3fYHTJVC5rSXq1ev5pprruHMM8/koYceanYSWJ/bbruNmpoarrvuOvbff39GjRpFRORcnhNBDjY0r2m27rrrLoYOHUptbS3HHXccJ5xwQovKmzBhAkOHDqV///48+uijLY4PUtXP66+/vkVl5Fod3pB8tIs+8MADVFVV0aFDh4ars7vuuiunsurq6liwYAFr1qwhIvj4449b9Ef+u9/9juuuu47Ro0dzzz335FwOfPZcnTp1Kvvuuy8A7dq1Y+DAgUyfPr1Fn5GrzGaTtn5R9OGHH/KDH/yAZcuWsf/++zfMc5CLZ599lmOOOYYLLriAu+++m9mzZ7fo/9GJoIQ222wzhg0bRvv27TnnnHNYsmQJ77//fk5lPfLIIxx88MHMmTOHMWPGMH78+BbHV1tb26KTtV59dXj16tW8/vrrdOjQIeey8tUu+swzz7By5Uo++OADPvjgAw499FD222+/nMoaMGAAffv25Rvf+Abjx49nwoQJdOyY22jU+++/n0ceeYSTTz6ZSy+9lFGjRlFXV5dTWeuzcOFCevXq1bDeq1cvnnrqqbyV31yZNZV8XxSVo65duzJ69Gj++te/cvXVV3POOeewdu3apg9cj6qqKrbddlsAqqurOfDAA3nkkUdyjs2JoISGDBnSsPzRRx+x0047sckmm+RU1qBBgxg0aBCQmp5v++23b3F8N9xwA0ceeWSLyshndbi+XfQ3v/lNi9tFTz311IZZq959912qq6tblKAmTZrEwoULmTRp0qe+aLN1xx13sOOOOwLQvn17Pve5z/H3v/895/Iy1dXV0alTp4b1mpqahnbnYsqsqeTzoqg1OP300+nUqRNvvfVWTsf369ePN998s2G9U6dOOV98gBNB2bjjjjs477zzcj6+vrll6dKl3HzzzS2eSu/uu+/mwAMPzHmu13r5rA7ns100PZHMnDmzRU19dXV1jB49moULF3L88cfz9a9/nTVr1uRU1ocffsiKFSsa1jfffHNefvnlnGPL1K1bt0/VMOrq6ujWrVveys9VPi+KWouePXvStWvXnI496qijmDNnDuvWrQNSTUWHHHJIzrE4EZSBFStW8Pzzz3P88ce3qJxVq1Zx1VVXMX/+fE488cScy/nggw+YP38+e+yxR4vigfxWh/PdLlrvzjvv5Gtf+1rOx8+cOZNtt92WTp06MX78eLbaaiuefPLJnMo64IADuP3226mrq2PdunW89tprLe5TSTdo0CBqa2sb1mtraxkwYEDeys+Hll4UlauXXnqJxYsXA7B48WJ22WWXZl/F18+c9thjj/H000/Ts2dPfvjDH3LmmWcyadIkDjroIPbcc8+cY/PtoyW2bt06Jk6cmJfJsDt37szYsWMZPXo0O+ywAytXrmTTTTfNupxrr72WU0/Nbaq/xpx++un87Gc/46233mKbbbbJ+viqqqqG49LbRVvSfBURvPzyyy1qSuvevTvvvPNOw/o222zD1ltvnVNZRxxxBK+88gpnnHEGu+++O6+++iq77757zrFlOvroo/n+978PpP7t8+bN47LLLstb+S1Vf1GUjz6uUsuc9vKBBx7gggsuYPTo0dTU1HDllVc2u6z1zZx2wgkn5K0vxYkgB/mc13TixImMHDmSDh06UFdXx4MPPshBBx3Uovi6devGzjvvnHOzzq233trQhPPGG2/Qvn17Fi9enJdk1ZLqcL9+/Vi0aFHDekvbRSHVyf7FL36xRWUs7bQ9973ejm77n8JmXTdl2K5DWtRPUP9F/eijjzJp0qSGu3xysb5z9eijj+bKK69k5cqV/PSnP6VLly45l59P+bwoKgefnUv5MN58c2TW5eR7Os71cSLI0vTHlzBhxmvU7n02Pbp25IWPupJrGrj22ms588wz+eEPfwik/hAeeOCBnMpavnw5VVVVdOnShffee4++ffs2dIZma/bs2Q3L48aNo0+fPpx00kk5lfXSSy9RXV1N7969s64OZzrqqKM4/PDDWbduHe3atePZZ5/l3HPPzamsen/7299a1CxU/0daNXgEXYC1wCxVMf3xJS36I122bBljxoxh6tSptGuXewvu+q4kTzvttJzLK6RCXBSVSj6/vIsxc5r7CLKQ7zlNN9/9EPYefw/b/ugvDLlkJtPmvcree++dU1nTpk1jwIABXHrppdx0001cfvnlOZWTb/fccw977LEHl1xyCdOmTcuqOpwp3+2iAHPmzOHLX/5yzsfnc85cgFseXES/b55Hn4O+zQd7j+S1quyb0OqV8xy8mW3e9RdFPXr0oLq6ms6dO+d8IVMO8nleFGPmtKLUCCRVA2OBx4CdgV9ExLpk3wHA5wEBD0XEw8WIKRf5zMz5ru5tvvsh9PrO9kx8ezU91nRky/+sZniON4Okaj31z2QfylmDcp+YY8s9DmPHH+zItStW06O6I32eW87w3XK7G2T640u4eklPajfpxrzlHVs0YUjDv3Hvs9n/1//K+bnz+fwjnf74Ei7869N82GsPuvXZh+XQonOinOfg/WyzySENd8C0Bfk8L3p07ciS9RyXz5nTilUjOA1YEhG3A8uAYwEkVQG/Aq4EfgOUdQ9RsSbKzlY+r/xcVnbyOb3hhBnP8SHVqHqjhm0tqV2U6xy85VxTyZd8nhfFmDmtWIlgL+CJZPlJ4LBkuTewNBLAx5J2KFJMWSvXibLzmVRcVnby+Uea7y/ucp2DN9/NaeUon+dFPqfj3JBidRZvDdQ/hWsVsNV6tqfveyn9YEkjgZEAvXv3LmigjTlrWP9PNedAyybKzld1L59fIC4rO/mc3jDfTQD5PF/zqVxrKvmU72kvCz1zWrESwTtAfc9PDbB0Pdsz9zWIiMnAZIDBgwfn/oi9Fsrnf265JhWXlb18/ZHm+4u7XOfgLUabdzloTdNeFqtpaAYwKFkeCMyUtGVEPA90VgKoiYgXihRTTobv1pMHzjmAl39xGA+cc0CLMny+qnv5rIa6rNIpRBNAvs7XfCrX338lU0ueYd3sD5HaARcB84EBwHTgxxExQtKXgfoHjTwcEfc3VtbgwYNj7ty5hQy3Vfr0nT4tu/JzWVZo/v0Xn6R5ETF4vfuKkQjyyYnAzCx7jSUCDygzM6twTgRmZhXOicDMrMI5EZiZVTgnAjOzCtfq7hqS9DbwnxwP34L1DFgrA44rO44re+Uam+PKTkvi2i4iuq9vR6tLBC0hae6Gbp8qJceVHceVvXKNzXFlp1BxuWnIzKzCORGYmVW4SksEk0sdwAY4ruw4ruyVa2yOKzsFiaui+gjMzOyzKq1GYGZmGZwIrNWRtLek/5Lkx1U2QtKAZDrYsuK4spMZVyHO/4pIBJKqJV0s6UhJP0kei11ykrpIuknSS5KuT+ZkKBuSvijp6lLHkU7Sd4GvRcSfIqJsJrmVtI2ksyUdJekySe1LHM9ewEPARuV0/mfEVTbnf3pcadtKfv5nxlWo878svhCL4DRgSUTcDiwDji1xPPUOBk4FdgK+COxZ2nA+IakrsD/QocShNJDUF/gOMLbUsazHfwPPRcQ0IEjNu1EyEfEQ8HayWjbnf0ZcZXP+Z8RVNud/elyFPP8rJRHsBTyRLD8JHFa6UD7lrxGxOiI+AhaSmrqzXBwD/F+pg8gwAngLOFfSTEmfK3VAaWYDF0r6CrCG1HlWLnz+Z6+izv9KSQRbA6uS5VXAViWMpUHyB4CkDsBrEbGoxCEBIOkY4HZSV7blZDvg6oj4GXAtcE6J42kQEY8CdwA3Ac9GxMclDimdz/8sVOL5XymJ4B2gJlmuofyeIXIc5dXccTKpE20ycICk/y1xPPWW88kf57NA2XQWS9oPeBfYHThT0qBGDygun//Zqbjzv1ISwQyg/g9zIDCzhLF8iqTDgL9HxHuStit1PAARcVhEDAdGAvdGxKUlDqnePcBuyfJmpObALhdfBF6IiDeB64AdShxPOp//WajE879SEsGNQG9JI4BtgakljgcASf8FXA3MkvQM5dN2W5Yi4m6gvaRvA/sAvypxSOmmkrp6PBLoCtxVymAkDQa6k+qQLZvzPz2ucjr/M35fZSM9rkKe/x5ZbGZW4SqlRmBmZhvgRGBmVuGcCMzMKpwTgZlZhXMisDZNUntJQ0sdh1k5qy51AGa5ktQL+DGwPfAK0InUKNopETEtuS99MrAvnwyoajUkPQr8MSIuL3Us1rY5EVirJGl3UgOjbgSOiIi1yfZdgHsl7RcRYyTdTCoRlLXkMcNnZgxemkh5PbPI2iiPI7BWR9JGpIbYC9gxItZl7D8VuAb4FrAxMDEiyrZGkDwWeiKwS0TsV+JwrAK5j8Bao4NJPcJhemYSSNwGrANG12+QNFLSq5LeknR5/XwBknpL+rWk4yT9UdLlacf8t6RLJE2V9ICkXSRtJOlESX+XdLykOyW9LmkfSS9IekfSTsnx20t6TtLwZH0/SVdJOlnSLckoZEg9/mEvYDtJ50v6vKQaSccmo0jr4+kgaayk70k6L3kC5QHJvr6SpkiaJembkh6UtFTScfn7tVubFRH+8U+r+gHOJvXwrTMaec+bwHvAScBa4L9IXfh8HfgYuDh536XA2clyO+D0ZHl/4JK08v4KPE2qFtI9+fypwOaknvdfDRyVlN0rOWZjYFJaGc8Bo5PlrwHvpO27Hrgvbf0g4FXg+rRtNwDnpa0fkHzensn6L0k9YG5Isn4l8HKp/7/8U/4/rhFYa1RfC2hsRqv0fe0iNaPTuoi4A5gOnJDsWwRcIOl/kvVrktcfAFtLOkfSOaQSy+tA54ion8DknxHxTkRMidRjp28HXgLOSPYfQ+qx1PWOA/4gaXNgD6DbhoKP1HNlXmz4x0jdk5hnpb3nXlLJ6cxk02pgVUQ8nKw/TurRxWaNcmextUYLktft17dTUg2pK/VHNnD8k8DhyfLVpK7wrwK+B5wCPAbsSOpq/s5G4vhUB1tEhKQrgLGSLgb2iYjvpr3lbVKPW55HavrBbNR/oXfO2P480HsDx6yj8WRpBriPwFqnfwIvAMO1/snGDyd1bm9ovtn2fJJM+kTERUB/4DXgn8lEKW+RanppIGlTSZ9vIrbrSc0vexFpd/xI6gT8G/h3RNwEfNhEOZleJJV4ds3Y3pFUk5NZzpwIrNVJmmFGkHom+/j0Sc+TsQO/JDWW4A9p26vSXg8DLkx2fVdSTUS8Anyb1FiEauBmYLSk70vqkdyuOhF4Me3zPnO1HRF1pMYujAT+mLZrF1KPgG6XHL9PEk83SdWk+jO2TLb1SI5pl/wQEcuBScD30jq6u5JqYpqQ9v509f9m1wqsUb591FotST1JTdfXG/gPqYnGewA3RMStae+ZSKrDeCGp5HFnRNyV7L+e1B1IfwA+ByyIiD8lCeMCUpOrbwrcB4wB3iA1gfivgb8DYyNibkZc2wKnRcSFads2JjWV5T5JWb8mlSieINWRvUuy/+Hkc3sCvyc12fypEXF/kgDOB/qRav/vA9wcEbMl7UBqTMXupPom5iXHHwqMjIgpufyOrTI4EZiZVTg3DZmZVTgnAjOzCudEYGZW4ZwIzMwqnBOBmVmFcyIwM6twTgRmZhXOicDMrMI5EZiZVbj/D0LFR+Uq606kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "可视化显示杠杆数据：leverage\n",
    "'''\n",
    "influ1_1.plot_index()\n",
    "influ1_1.plot_influence()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dfb_Intercept</th>\n",
       "      <th>dfb_X</th>\n",
       "      <th>cooks_d</th>\n",
       "      <th>standard_resid</th>\n",
       "      <th>hat_diag</th>\n",
       "      <th>dffits_internal</th>\n",
       "      <th>student_resid</th>\n",
       "      <th>dffits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.296351</td>\n",
       "      <td>0.291164</td>\n",
       "      <td>0.062977</td>\n",
       "      <td>-0.724685</td>\n",
       "      <td>0.193440</td>\n",
       "      <td>-0.354899</td>\n",
       "      <td>-0.712699</td>\n",
       "      <td>-0.349029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.082303</td>\n",
       "      <td>0.080896</td>\n",
       "      <td>0.004955</td>\n",
       "      <td>-0.199161</td>\n",
       "      <td>0.199886</td>\n",
       "      <td>-0.099545</td>\n",
       "      <td>-0.192663</td>\n",
       "      <td>-0.096297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.039489</td>\n",
       "      <td>0.038337</td>\n",
       "      <td>0.001747</td>\n",
       "      <td>-0.170819</td>\n",
       "      <td>0.106927</td>\n",
       "      <td>-0.059106</td>\n",
       "      <td>-0.165187</td>\n",
       "      <td>-0.057158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.012184</td>\n",
       "      <td>-0.011790</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.058649</td>\n",
       "      <td>0.097878</td>\n",
       "      <td>0.019318</td>\n",
       "      <td>0.056667</td>\n",
       "      <td>0.018665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.015938</td>\n",
       "      <td>-0.015283</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.098218</td>\n",
       "      <td>0.082606</td>\n",
       "      <td>0.029473</td>\n",
       "      <td>0.094918</td>\n",
       "      <td>0.028482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.016174</td>\n",
       "      <td>0.015404</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>-0.115585</td>\n",
       "      <td>0.076383</td>\n",
       "      <td>-0.033240</td>\n",
       "      <td>-0.111716</td>\n",
       "      <td>-0.032127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.013731</td>\n",
       "      <td>-0.012778</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>0.143297</td>\n",
       "      <td>0.066764</td>\n",
       "      <td>0.038328</td>\n",
       "      <td>0.138533</td>\n",
       "      <td>0.037053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.012683</td>\n",
       "      <td>-0.011715</td>\n",
       "      <td>0.000741</td>\n",
       "      <td>0.145662</td>\n",
       "      <td>0.065292</td>\n",
       "      <td>0.038498</td>\n",
       "      <td>0.140822</td>\n",
       "      <td>0.037219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.031494</td>\n",
       "      <td>0.028666</td>\n",
       "      <td>0.006066</td>\n",
       "      <td>-0.423474</td>\n",
       "      <td>0.063367</td>\n",
       "      <td>-0.110147</td>\n",
       "      <td>-0.411583</td>\n",
       "      <td>-0.107054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.016197</td>\n",
       "      <td>0.014823</td>\n",
       "      <td>0.001459</td>\n",
       "      <td>-0.206608</td>\n",
       "      <td>0.063971</td>\n",
       "      <td>-0.054012</td>\n",
       "      <td>-0.199886</td>\n",
       "      <td>-0.052255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.008475</td>\n",
       "      <td>-0.011123</td>\n",
       "      <td>0.004957</td>\n",
       "      <td>-0.395459</td>\n",
       "      <td>0.059612</td>\n",
       "      <td>-0.099567</td>\n",
       "      <td>-0.384057</td>\n",
       "      <td>-0.096696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.830612</td>\n",
       "      <td>0.916534</td>\n",
       "      <td>0.469534</td>\n",
       "      <td>3.707950</td>\n",
       "      <td>0.063934</td>\n",
       "      <td>0.969055</td>\n",
       "      <td>12.403693</td>\n",
       "      <td>3.241645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.001210</td>\n",
       "      <td>0.001239</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.004188</td>\n",
       "      <td>0.139580</td>\n",
       "      <td>0.001687</td>\n",
       "      <td>0.004046</td>\n",
       "      <td>0.001630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.228887</td>\n",
       "      <td>-0.235387</td>\n",
       "      <td>0.055521</td>\n",
       "      <td>-0.907110</td>\n",
       "      <td>0.118903</td>\n",
       "      <td>-0.333231</td>\n",
       "      <td>-0.901425</td>\n",
       "      <td>-0.331142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.250784</td>\n",
       "      <td>-0.255962</td>\n",
       "      <td>0.051589</td>\n",
       "      <td>-0.705024</td>\n",
       "      <td>0.171896</td>\n",
       "      <td>-0.321214</td>\n",
       "      <td>-0.692692</td>\n",
       "      <td>-0.315595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.095732</td>\n",
       "      <td>-0.097443</td>\n",
       "      <td>0.007046</td>\n",
       "      <td>-0.230491</td>\n",
       "      <td>0.209638</td>\n",
       "      <td>-0.118707</td>\n",
       "      <td>-0.223071</td>\n",
       "      <td>-0.114886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.111513</td>\n",
       "      <td>-0.113443</td>\n",
       "      <td>0.009370</td>\n",
       "      <td>-0.257820</td>\n",
       "      <td>0.219922</td>\n",
       "      <td>-0.136893</td>\n",
       "      <td>-0.249632</td>\n",
       "      <td>-0.132546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    dfb_Intercept     dfb_X   cooks_d  standard_resid  hat_diag  \\\n",
       "0       -0.296351  0.291164  0.062977       -0.724685  0.193440   \n",
       "1       -0.082303  0.080896  0.004955       -0.199161  0.199886   \n",
       "2       -0.039489  0.038337  0.001747       -0.170819  0.106927   \n",
       "3        0.012184 -0.011790  0.000187        0.058649  0.097878   \n",
       "4        0.015938 -0.015283  0.000434        0.098218  0.082606   \n",
       "5       -0.016174  0.015404  0.000552       -0.115585  0.076383   \n",
       "6        0.013731 -0.012778  0.000735        0.143297  0.066764   \n",
       "7        0.012683 -0.011715  0.000741        0.145662  0.065292   \n",
       "8       -0.031494  0.028666  0.006066       -0.423474  0.063367   \n",
       "9       -0.016197  0.014823  0.001459       -0.206608  0.063971   \n",
       "10       0.008475 -0.011123  0.004957       -0.395459  0.059612   \n",
       "11      -0.830612  0.916534  0.469534        3.707950  0.063934   \n",
       "12      -0.001210  0.001239  0.000001        0.004188  0.139580   \n",
       "13       0.228887 -0.235387  0.055521       -0.907110  0.118903   \n",
       "14       0.250784 -0.255962  0.051589       -0.705024  0.171896   \n",
       "15       0.095732 -0.097443  0.007046       -0.230491  0.209638   \n",
       "16       0.111513 -0.113443  0.009370       -0.257820  0.219922   \n",
       "\n",
       "    dffits_internal  student_resid    dffits  \n",
       "0         -0.354899      -0.712699 -0.349029  \n",
       "1         -0.099545      -0.192663 -0.096297  \n",
       "2         -0.059106      -0.165187 -0.057158  \n",
       "3          0.019318       0.056667  0.018665  \n",
       "4          0.029473       0.094918  0.028482  \n",
       "5         -0.033240      -0.111716 -0.032127  \n",
       "6          0.038328       0.138533  0.037053  \n",
       "7          0.038498       0.140822  0.037219  \n",
       "8         -0.110147      -0.411583 -0.107054  \n",
       "9         -0.054012      -0.199886 -0.052255  \n",
       "10        -0.099567      -0.384057 -0.096696  \n",
       "11         0.969055      12.403693  3.241645  \n",
       "12         0.001687       0.004046  0.001630  \n",
       "13        -0.333231      -0.901425 -0.331142  \n",
       "14        -0.321214      -0.692692 -0.315595  \n",
       "15        -0.118707      -0.223071 -0.114886  \n",
       "16        -0.136893      -0.249632 -0.132546  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "强影响诊断的统计量汇总：\n",
    "(1) dfb_Intercept：dfbetas的截距项部分 \n",
    "(2) dfb_X：dfbetas自变量回归系数部分的值\n",
    "(3) cooks_d：库克距离\n",
    "(4) standard_resid：标准化残差，或内学生化残差\n",
    "(5) hat_diag：帽子矩阵对角线元素向量\n",
    "(6) dffits_internal：使用内学生化残差计算的dffits\n",
    "(7) student_resid：外学生化残差\n",
    "(8) dffits：使用外学生化残差计算的dffits\n",
    "'''\n",
    "influ1_1.summary_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4.4 多重共线性\n",
    "回归变量之间不存在线性关系，即称之为为正交的。如果回归变量存在线性关系，那么回归模型的推断就有可能出现问题。比如鉴定回归变量的相对影响，通过模型进行预测或估计等等。"
   ]
  },
  {
   "attachments": {
    "4.%E5%9B%9E%E5%BD%92%E5%88%86%E6%9E%9018_1.jpg": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- #### 例3：多重共线性的测量<br>\n",
    "![4.%E5%9B%9E%E5%BD%92%E5%88%86%E6%9E%9018_1.jpg](attachment:4.%E5%9B%9E%E5%BD%92%E5%88%86%E6%9E%9018_1.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\users\\hp\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\scipy\\stats\\stats.py:1542: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=12\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.881</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   14.52</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 16 Aug 2021</td> <th>  Prob (F-statistic):</th>  <td>0.00499</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:12:15</td>     <th>  Log-Likelihood:    </th> <td> -13.236</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    12</td>      <th>  AIC:               </th> <td>   40.47</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     5</td>      <th>  BIC:               </th> <td>   43.87</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   26.6531</td> <td>   14.033</td> <td>    1.899</td> <td> 0.116</td> <td>   -9.419</td> <td>   62.726</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X[0]</th>      <td>   -1.5306</td> <td>    1.339</td> <td>   -1.143</td> <td> 0.305</td> <td>   -4.974</td> <td>    1.913</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X[1]</th>      <td>   -1.8378</td> <td>    1.416</td> <td>   -1.298</td> <td> 0.251</td> <td>   -5.478</td> <td>    1.803</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X[2]</th>      <td>   -1.7747</td> <td>    1.414</td> <td>   -1.255</td> <td> 0.265</td> <td>   -5.409</td> <td>    1.859</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X[3]</th>      <td>   -1.8433</td> <td>    1.402</td> <td>   -1.315</td> <td> 0.246</td> <td>   -5.448</td> <td>    1.761</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X[4]</th>      <td>    1.0232</td> <td>    0.391</td> <td>    2.617</td> <td> 0.047</td> <td>    0.018</td> <td>    2.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X[5]</th>      <td>    5.0470</td> <td>    0.728</td> <td>    6.936</td> <td> 0.001</td> <td>    3.177</td> <td>    6.918</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.691</td> <th>  Durbin-Watson:     </th> <td>   2.381</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.429</td> <th>  Jarque-Bera (JB):  </th> <td>   0.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.038</td> <th>  Prob(JB):          </th> <td>   0.928</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.542</td> <th>  Cond. No.          </th> <td>    234.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.946\n",
       "Model:                            OLS   Adj. R-squared:                  0.881\n",
       "Method:                 Least Squares   F-statistic:                     14.52\n",
       "Date:                Mon, 16 Aug 2021   Prob (F-statistic):            0.00499\n",
       "Time:                        12:12:15   Log-Likelihood:                -13.236\n",
       "No. Observations:                  12   AIC:                             40.47\n",
       "Df Residuals:                       5   BIC:                             43.87\n",
       "Df Model:                           6                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     26.6531     14.033      1.899      0.116      -9.419      62.726\n",
       "X[0]          -1.5306      1.339     -1.143      0.305      -4.974       1.913\n",
       "X[1]          -1.8378      1.416     -1.298      0.251      -5.478       1.803\n",
       "X[2]          -1.7747      1.414     -1.255      0.265      -5.409       1.859\n",
       "X[3]          -1.8433      1.402     -1.315      0.246      -5.448       1.761\n",
       "X[4]           1.0232      0.391      2.617      0.047       0.018       2.028\n",
       "X[5]           5.0470      0.728      6.936      0.001       3.177       6.918\n",
       "==============================================================================\n",
       "Omnibus:                        1.691   Durbin-Watson:                   2.381\n",
       "Prob(Omnibus):                  0.429   Jarque-Bera (JB):                0.150\n",
       "Skew:                           0.038   Prob(JB):                        0.928\n",
       "Kurtosis:                       3.542   Cond. No.                         234.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=np.array([10.006, 9.737, 15.087, 8.422, 8.625, 16.289,\n",
    "            5.958, 9.313, 12.960, 5.541, 8.756, 10.937])\n",
    "x1=np.repeat([8, 0, 2, 0], [3, 3, 3, 3])\n",
    "x2=np.repeat([1, 0, 7, 0], [3, 3, 3, 3])\n",
    "x3=np.repeat([1, 9, 0], [3, 3, 6])\n",
    "x4=np.repeat([1, 0, 1, 10], [1, 2, 6, 3])\n",
    "x5=np.array([0.541, 0.130, 2.116, -2.397, -0.046, 0.365,\n",
    "             1.996, 0.228, 1.38, -0.798, 0.257, 0.440])\n",
    "x6=np.array([0.099, 0.070, 0.115, 0.252, 0.017, 1.504,\n",
    "             -0.865, -0.055, 0.502, -0.399, 0.101, 0.432])\n",
    "X = np.c_[x1,x2,x3,x4,x5,x6]\n",
    "XY = np.c_[X,y]\n",
    "formula='y~X'\n",
    "result=smf.ols(formula,data={'y':y,'X':X}).fit()\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "自变量矩阵的条件数为: 2180.7967\n",
      "\n",
      "最小特征值 0.0011065466503663475 \n",
      "对应的特征向量是 [-0.44701145 -0.41991274 -0.54347799 -0.57308725 -0.0072682   0.00197808]\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "使用R语言的Kappa函数求的条件数，多重共线性用于测量多个自变量之间是否存在线性关系\n",
    "'''\n",
    "dataFrame = pd.DataFrame(X)\n",
    "cor_X = dataFrame.corr()#自变量矩阵的相关系数矩阵\n",
    "eigs = np.linalg.eigh(cor_X)[0]#特征值，使用eigh函数，特征值从小到大排列\n",
    "eigv = np.linalg.eigh(cor_X)[1]#特征向量矩阵，特征值eigs[i]的相应特征向量是eigv[:,i]\n",
    "cond = np.max(eigs)/np.min(eigs)#用最大特征值除以最小特征值\n",
    "#特征值的另一种计算方法，结果一样，和Statsmodels不同，不对结果开平方。\n",
    "#cond = np.linalg.norm(cor_X,ord=2)*np.linalg.norm(np.linalg.inv(cor_X),ord=2)\n",
    "print('自变量矩阵的条件数为:',np.round(cond,4))\n",
    "#缺省情况下第1个是最小特征值，相应特征向量也是矩阵的第1列。\n",
    "print('\\n最小特征值',eigs[0],'\\n对应的特征向量是',eigv[:,0])\n",
    "#betas=np.round(np.array(eigv[:,0]),7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(234.0, 54533.034)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Statsmodels计算条件数：\n",
    "(1) 获取自变量的设计矩阵，即在自变量矩阵里加入一个元素为1的常数列\n",
    "(2) 求设计矩阵转置与其自身的点积，该点积是一个对称矩阵\n",
    "(3) 求点积的特征值与特征向量\n",
    "(4) 用最大特征值除以最小特征值，即通常意义上的条件数\n",
    "(5) Statsmodels模型的条件数是该值的平方根\n",
    "'''\n",
    "wx=result.model.wexog\n",
    "wxx=np.dot(wx.T,wx)\n",
    "eig,eigv=np.linalg.eigh(wxx)\n",
    "cond1=np.max(eig)/np.min(eig)\n",
    "np.round(cond1**0.5,0),np.round(cond1,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\users\\hp\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\scipy\\stats\\stats.py:1542: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=12\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.701</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.634</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   10.54</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 16 Aug 2021</td> <th>  Prob (F-statistic):</th>  <td>0.00438</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:12:17</td>     <th>  Log-Likelihood:    </th> <td> -23.479</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    12</td>      <th>  AIC:               </th> <td>   52.96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>     9</td>      <th>  BIC:               </th> <td>   54.41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td>    8.9557</td> <td>    0.755</td> <td>   11.859</td> <td> 0.000</td> <td>    7.247</td> <td>   10.664</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(x5 ** 2)</th> <td>    0.2997</td> <td>    0.295</td> <td>    1.016</td> <td> 0.336</td> <td>   -0.368</td> <td>    0.967</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>         <td>    5.0292</td> <td>    1.096</td> <td>    4.590</td> <td> 0.001</td> <td>    2.551</td> <td>    7.508</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.959</td> <th>  Durbin-Watson:     </th> <td>   2.604</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.084</td> <th>  Jarque-Bera (JB):  </th> <td>   1.732</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.457</td> <th>  Prob(JB):          </th> <td>   0.421</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.621</td> <th>  Cond. No.          </th> <td>    5.14</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.701\n",
       "Model:                            OLS   Adj. R-squared:                  0.634\n",
       "Method:                 Least Squares   F-statistic:                     10.54\n",
       "Date:                Mon, 16 Aug 2021   Prob (F-statistic):            0.00438\n",
       "Time:                        12:12:17   Log-Likelihood:                -23.479\n",
       "No. Observations:                  12   AIC:                             52.96\n",
       "Df Residuals:                       9   BIC:                             54.41\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      8.9557      0.755     11.859      0.000       7.247      10.664\n",
       "I(x5 ** 2)     0.2997      0.295      1.016      0.336      -0.368       0.967\n",
       "x6             5.0292      1.096      4.590      0.001       2.551       7.508\n",
       "==============================================================================\n",
       "Omnibus:                        4.959   Durbin-Watson:                   2.604\n",
       "Prob(Omnibus):                  0.084   Jarque-Bera (JB):                1.732\n",
       "Skew:                           0.457   Prob(JB):                        0.421\n",
       "Kurtosis:                       4.621   Cond. No.                         5.14\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "去掉共线性严重的x1,x2,x3,x4之后再进行模型拟合\n",
    "'''\n",
    "formula2 = 'y~I(x5**2)+x6'\n",
    "result2 = smf.ols(formula2,data=dataFrame).fit()\n",
    "result2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ##### 结果解读：\n",
    "(1) 无论是使用R语言Kappa函数相同的方法计算的条件数，还是使用StatsModels计算条件数的平方，其值远大于1000，因此使用全部自变量进行拟合的模型存在严重的多重共线性问题。\n",
    "\n",
    "(2) 通过使用最小特征值对应的特征向量构建的方程，可以看出x1,x2,x3,x4四个存在共线性，因此有必要进行处理，比如可以只使用其中一个变量和x5,x6等剩下变量拟合模型，或者干脆全部去掉。\n",
    "\n",
    "(3) 实验结果是全部去掉效果最好,因为加入其中任何一个，其系数的t检验都不显著。\n",
    "\n",
    "(4) 使用全部自变量进行拟合的结果可以看出，回归参数皆不显著，模型拟合效果很差。\n",
    "\n",
    "(5) 去掉x1,x2,x3,x4只使用x5,x6进行拟合，模型效果不错。虽然在对残差进行正态性检验的效果不是很好，但是这个问题可以通过增加样本数据解决，毕竟现存模型只有12条数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------"
   ]
  }
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